STATIC AND DYNAMIC THERMAL BEHAVIOR OF CARBON
BASED NANOFLUIDS
Thesis
Submitted to
The School of Engineering of the
UNIVERSITY OF DAYTON
In Partial Fulfillment of the Requirements for
The Degree of
Master of Science in Mechanical Engineering
By
Omar Hashim Al Samarrai
Dayton, Ohio
May, 2013
STATIC AND DYNAMIC THERMAL BEHAVIOR OF CARBON
BASED NANOFLUIDS
Name: Al Samarrai, Omar Hashim
APPROVED BY:
Khalid Lafdi, Ph.D
Advisory Committee Chairman
Professor, Department of
Chemical and Materials Engineering
Kevin Hallinan, Ph.D,
Committee Member
Professor, Department of
Mechanical and Aerospace Engineering
Muhammad Usman, Ph.D
Committee Member
Assistant Professor, Department of
Mathematics
John G. Weber, Ph.D
Associate Dean
School of Engineering
Tony E. Saliba, Ph.D
Dean, School of Engineering
& Wilke Distinguished Professor
ii
ABSTRACT
STATIC AND DYNAMIC THERMAL BEHAVIOR OF CARBON
BASED NANOFLUIDS
Name: Al Samarrai, Omar Hashim
University of Dayton
Advisor: Dr. Khalid Lafdi
Nanofluids are a new class of heat transfer fluids which are engineered by
dispersing nanometer-sized solid particles or tubes in conventional heat transfer
fluids such as water, ethylene glycol, and engine oil.
The first part of this study includes carbon nanotube (CNT)-ethylene glycol
(EG) suspension as thermal management fluids. Three types of CNTs with
various degrees of crystallinity and surface energy were prepared using heattreatment temperature. The thermal conductivity of nanofluids tested at varying
concentration from 0% to 1.2% using static and dynamic thermal tests. The CNT
type and volume concentration were investigated at various shear rates. The
thermal resistance of the test suspensions decreased with increasing shear rate.
These tests showed that CNT with higher crystallinity and concentration
exhibit better thermal performance.
iii
However, these CNT tend to break down under high shear. Conversely, CNT
with medium crystallinity exhibits the best compromise.
The second part of the study includes the formulation of a theoretical model
for the effective thermal conductivity of nanofluids. The model is based on a
novel point of view regarding the arrangement of nanoparticles in the base fluid.
The predictions from the model show a reasonably good agreement with the
experimental results.
iv
To my parents
To my wife
To my three little angels, Mohammad, Rand and Hashim
With love
v
ACKNOWLEDGEMENTS
First of all I would like to thank almighty Allah whose blessings made this
work possible. Without His will kindness and mercy, the completion of this work
would have never been possible.
It is my pleasure to thank those who made this research possible. I would
like to express my sincere gratitude to my supervisor Professor Dr. Khalid Lafdi
for his valuable guidance and advice. He has been very supportive and patient
throughout the progress of my thesis. I would like to express my appreciation to
the other members of my advisory committee, Dr. Kevin Hallinan and Dr.
Muhammad Usman for their time and advice.
I would like to acknowledge my colleague Larry Funke for helping me in a
part of my experimental work and to Matt Boehle for his technical support.
Finally, my family has played an integral role in my graduate studies. I would
like to thank my parents for always believing in me and to whom I owe
everything. I am extremely fortunate to have my wife Rana, whose smiles
encouragements and endless patience have kept me going through it all. This
wouldn’t have been possible without her support.
Rana, I do not find enough words to thank you for what you did.
vi
TABLE OF CONTENTS
ABSTRACT ……………………………………………………………………..
iii
DEDICATION ……………………………………………………………………
v
ACKNOWLEDGEMENTS ……………………………………………………..
vi
LIST OF FIGURES …………………………………………………………….
x
LIST OF TABLES ………………………………………………………………
xii
LIST OF SYMBOLS ……………………………………………………………
xiii
CHAPTER I
MOTIVATION ………………………………………………...
1
CHAPTER II
LITERATURE REVIEW ……………………………………..
6
2.1.
Introduction …………………………………………………...
6
2.2.
Nanofluids Preparation ………………………………………
7
2.2.1.
One-Step Method …………………………………………….
7
2.2.2.
Two-Step Method …………………………………………….
8
2.2.3.
Stability ………………………………………………………..
8
2.3.
Effective Parameters on Thermal Conductivity …………...
11
2.3.1.
Particle Size …………………………………………………..
11
2.3.2.
Particle Shape ………………………………………………..
13
2.3.3.
Base Fluid …………………………………………………….
13
2.3.4.
Temperature-Dependent Thermal Conductivity …………..
14
vii
2.4.
Mechanisms of Thermal Conduction Enhancement ……..
15
2.4.1
The Brownian Motion ………………………………………..
16
2.4.2
Molecular-level Layering …………………………………….
17
2.4.3.
Clustering …………………………………………………….
19
2.5.
Carbon Nanotubes …………………………………………..
21
2.6.
Modeling Studies …………………………………………….
26
2.7.
Measurement of Thermal Conductivity of Liquids ………..
31
2.7.1.
Transient Hot Wire Method (THW) …………………………
31
2.7.2.
Steady-State Parallel-Plate Method ………………………..
32
2.7.3.
Temperature Oscillation Method …………………………...
33
2.8.
Dynamic Thermal Test ………………………………………
34
2.8.1.
Shear Flow Test ……………………………………………...
34
2.8.2
Pipe Flow Test ………………………………………………..
35
CHAPTER III
MATERIALS AND CHARACTERIZATION METHODS ….
37
3.1.
Materials Preparation ………………………………………..
37
3.2.
Electron Microscopy …………………………………………
37
3.3.
Raman Spectroscopy and X-rays ………………………….
39
3.4.
Electrical Analysis ……………………………………………
40
3.5.
Volume Resistivity ……………………………………………
41
3.6.
Preparation of Nanofluids …………………………………...
43
3.6.1.
Static and Dynamic …………………………………………..
43
3.6.2.
Pipe Flow ……………………………………………………..
44
3.7.
Experimental Setup and Procedures ………………………
45
viii
3.7.1.
Static Test …………………………………………………….
45
3.7.1.1
Static Test Apparatus ………………………………………..
45
3.7.1.2.
Static Test Procedure ………………………………………..
47
3.7.2.
Dynamic Shear Test …………………………………………
47
3.7.2.1
Dynamic Shear Test Apparatus …………………………….
48
3.7.2.2.
Dynamic Shear Test Procedure ……………………………
51
3.7.3.
Pipe Flow Test ………………………………………………..
52
3.7.3.1.
Pipe Flow Apparatus ………………………………………...
52
3.7.3.2.
Pipe Flow Data Acquisition ………………………………….
57
3.7.3.3.
Pipe Flow Test Procedure …………………………………..
57
3.8.
Results and Discussion ……………………………………..
59
3.8.1.
Materials Characterization …………………………………..
59
3.8.2.
Raman Analysis ……………………………………………...
63
3.8.3.
XRD Analysis …………………………………………………
64
3.9.
Thermal Conductivity of Nanofluid Samples ………………
67
3.9.1
Static …………………………………………………………..
67
3.9.2.
Dynamic Shear ……………………………………………….
71
3.9.3.
Pipe Flow ……………………………………………………..
81
CHAPTER IV
MODELING …………………………………………………...
86
CHAPTER V
CONCLUSIONS AND FUTURE RECOMMENDATIONS..
95
5.1.
Conclusions …………………………………………………..
95
5.2.
Future Recommendations …………………………………..
97
BIBLIOGRAPHY …………………………………………………………………
98
ix
LIST OF FIGURES
Figure (2-1) Schematic of Well-dispersed Aggregates ……………………….. 20
Figure (2-2) SWCNT and MWCNT ……………………………………………… 22
Figure (2-3) Steady State Parallel-plate Setup ………………………………… 33
Figure (2-4) Temperature Oscillation Setup ……………………………………. 34
Figure (3-1) Fixture for Testing CNTs Volume Resistivity ……………………. 42
Figure (3-2) KD2 Pro Thermal Property Analyzer and EchoTherm IC25XT
Chilling/ Heating Dry Bath …………………………………………. 46
Figure (3-3) The Probe of the KD2P and the Probe is Placed into the
Sample Glass Vial ………………………………………………….. 46
Figure (3-4) Schematic Illustration of the Dynamic Shear Test Section …….. 49
Figure (3-5) The Entire System ………………………………………………….. 49
Figure (3-6) The Dynamic Shear Test Apparatus ……………………………... 50
Figure (3-7) Nanofluid Pipe Flow Test Setup …………………………………..
53
Figure (3-8) Inside and Outside Thermocouple Installation ………………….. 54
Figure (3-9) Test Section Just Prior to Wrapping with Insulation ……………. 54
Figure (3-10) Inlet and Outlet Thermocouple Installation ………………………. 55
Figure (3-11) Heater Power Source and Voltage/Current Measuring Leads … 56
Figure (3-12) Bright-field Images of Pristine Carbon Nanotubes (AG) ……….. 59
Figure (3-13) Models of Various Nanotubes Configurations …………………… 60
x
Figure (3-14) Carbon Plane Structure as a Function of Heat-Treatment
Temperature [82] …………………………………………………… 61
Figure (3-15) Bright-field Micrograph of “Dixie Cup” CNTs Structure …………. 62
Figure (3-16) High-Resolution Imaging of Localized Area of “Dixie Cups”
Structure …………………………………………………………….. 63
Figure (3-17) Raman Spectra Records for AG, LHT and HHT-CNT, as well as
those after the Ozone Treatment and the Secondary CNT
Growth ……………………………………………………………….
64
Figure (3-18) XRD Image of Field Emission Test ……………………………….
65
Figure (3-19) Volume Resistivity of as Received CNTs at 108psi …………….
66
Figure (3-20) Percent Improvement in Thermal Conductivity versus wt%
Loading ………………………………………………………………
68
Figure (3-21) Comparing Milled Samples to Non-milled Samples of the Three
CNTs Tested ………………………………………………………..
70
Figure (3-22) Shows the Rotating Cylindrical System ………………………….
71
Figure (3-23) Percent Reduction in Thermal Resistance versus Concentration
for AG, LHT, and HHT Nanofluids ………………………………..
75
Figure (3-24) Percent Reduction in Thermal Resistance at (600 s-1) Shear
Rate ………………………………………………………………….
77
Figure (3-25) Comparison of Nanofluids with Milled Nanoparticles versus
Non-Milled Nanoparticles at (600 s-1) Shear Rate ……………… 79
Figure (3-26) Percent Improvement in Heat Transfer Coefficient at 0.2% wt
Loading ………………………………………………………………
83
Figure (4-1) SEM Picture of Nanotube Suspension …………………………... 87
Figure (4-2) Hexagonal Mesh of CNT …………………………………………..
88
Figure (4-3) Unit Cell of Hexagon Network …………………………………….
88
Figure (4-4) Compression between the Hexagonal Array Model and the
Experimental Results ………………………………………………
xi
94
LIST OF TABLES
Table (3-1)
Description of Tested Carbon Nanotubes …………………
65
Table (3-2)
La Values from Raman Test ………………………………...
80
xii
LIST OF SYMBOLS
A
effective area of the measuring electrode
A
inside surface area of the pipe
b
thickness of the node in hexagonal unit cell
B
line broadening at half the maximum intensity
dp
particle diameter
DE
decene
EG
ethylene glycol
h
heat transfer coefficient
La
average crystal lengths
Lc
average crystal heights
I
current
k
thermal conductivity
kn
thermal conductivity of the node in hexagonal unit cell
keff
effective thermal conductivity of suspension
ke,x
thermal conductivity components of the complex elliptical particle
along the x axes
xiii
ke,y
thermal conductivity components of the complex elliptical particle
along the y axes
ko
base fluid thermal conductivity
knf
nanofluid thermal conductivity
kbf
base fluid thermal conductivity
klayer
thermal conductivity of nanolayer
kp
particles thermal conductivity
kpe
modified thermal conductivity of particles
kpi
in-plane conductivity in the nanotubes bundle
kpt
through-plane conductivity in the nanotubes bundle
ke1
thermal conductivity of first layer in the hexagonal unit cell
ke2
thermal conductivity of first layer in the hexagonal unit cell
ke3
thermal conductivity of first layer in the hexagonal unit cell
L
height of the cylindrical section in shear test
L
Half of the length of the nanotubes bundle
L1
height of first layer in the hexagonal unit cell
L2
height of second layer in the hexagonal unit cell
L3
height of third layer in the hexagonal unit cell
MWCNTs
Multi-wall carbon nanotubes
n
Shape factor
xiv
Pv
volume resistivity
riAl
inside radius of the aluminum cylinder
roAl
outside radius of the aluminum cylinder
riCu
inside radius of the copper cylinder
roCu
outside radius of the copper cylinder
rb
thickness of the hexagonal side
R
nanocomposite electrical resistance
Relec_Heater electrical resistance of the heater
RthGlobal
Global thermal conductivity
Rshunt
electrical resistance of the shunt resistor
Rv
measured electrical resistance
Rth ( )
thermal resistance of the fluid in the gap
RT1
thermal resistance of the first layer in hexagonal unit cell
RT2
thermal resistance of the second layer in hexagonal unit cell
RT3
thermal resistance of the third layer in hexagonal unit cell
SWCNTs
single-wall carbon nanotubes
SEM
Scanning Electron Microscope
SSA
Specific surface area
TEM
Transmission Electron Microscope
t
sample thickness
t
time
T
temperature
xv
Tcooler
outer wall temperature (cooler temperature)
Theater
inner wall temperature (heater temperature)
Vp
particles volume in the hexagonal unit cell
Vf
fluid volume in the hexagonal unit cell
V
voltage
Vdrop
voltage drop across the shunt resistor
Vsupply
voltage across the heater
w
width of hexagonal unit cell
GREEK SYMBOLS
α
Outer radius to inner radius ratio
Ratio of the nanolayer thickness to the original particle radius
ρ
electric resistivity
σ
reciprocal of electrical resistivity
θ
Bragg angle
γ
ratio of nanolayer thermal conductivity to particle thermal conductivity
γ
shear rate
ψ
sphericity
xvi
ωo
angular velocity
ϕ
volume fraction
ϕ
heat flux
xvii
CHAPTER I
MOTIVATION
Cooling is one of the most important technical challenges facing numerous
diverse industries including microelectronics, transportation, chemical process,
air conditioning, and manufacturing. As the power density of these systems
increases, the demand of the efficient heat transfers systems increases. Many
attempts have been made to solve this problem. Techniques such as increasing
the flow or changing the system geometry to create an extended surface area,
such as fins and microchannels, create different problems.
Both of these
techniques require more power to overcome the pressure drop and larger
systems, which runs counter to the high efficiency, small size systems currently
being developed. The cooling fluids that have been traditionally used such as
water, ethylene glycol, and engine oil, have a rather low thermal conductivity and
low thermal heat transfer coefficient. There is a need to develop new types of
fluids that will be more effective in terms of heat exchange performance.
Various techniques have been proposed to enhance the heat transfer
performance of fluids. Researchers have tried to increase the thermal
conductivity of base fluids by suspending micro-sized solid particles in fluids
since the thermal conductivity of solids is higher than pristine fluids [1]
1
In conventional cases, the suspended particles are of micrometric or even
macrometric size. Such large particles may cause some severe problems such
as sedimentation, clogging flow channels, eroding pipes and channels. To be
efficient, these conventional fluid suspensions uses over 10 vol. % of solid
particles, resulting in significantly greater pressure drop and pump power [35]. In
recent years, modern technologies have permitted the manufacturing of particles
down to the nanometer scale, called nanoparticles, which are easily dispersible
in convectional heat transfer fluid and have shown enhancements in heat
transfer. Argonne National Laboratory has pioneered ultra-high thermal
conductivity fluids, called nanofluids. [2] Nanofluids are suspension containing
particles that are significantly smaller than 100 nm (nanoparticles). The thermal
conductivity of these nanofluids can be much higher than those of commercial
coolant. Nanoparticles have some unique properties, such as large surface area
to volume ratio; the surface area-to-volume ratio is 1000 times larger for particles
with a 10 nm diameter than for particles with a 10 μm diameter. The much larger
surface areas of nanoparticles should not only improve heat transfer capabilities,
but also increase the stability of the suspensions. Nanoparticles offer extremely
large total surface areas and therefore have great potential for application in heat
transfer.
Lee et al. [3] demonstrated that the thermal conductivity of metal oxide
nanofluids is significantly higher than that of the base fluids. Xuan and Li [4]
shows that adding Cu nanoparticles with 8% volume fraction to transformer oil
and water suspension improve the thermal conductivity of the suspension to
2
about 45% and 78% respectively. Eastman et al. [5] found that the enhancement
in thermal conductivity of nanofluids with metallic particles is much higher, as
compared to that of a macro-slurry of the same fluid and particle combination.
The thermal conductivity of the nanofluid is influenced by the heat transfer
properties of the base fluid and nanoparticle material, the volume fraction, the
size, and the shape of the nanoparticles suspended in the liquid, the temperature
of the nanofluid, as well as the distribution of the dispersed particles [6]. Many
researchers have reported the substantial increases in thermal conductivity with
increasing the nanoparticles concentration. Das et al. [10] observed a strong
dependence of the thermal conductivity enhancement on temperature of the
nanofluid, which is confirmed by Li and Peterson [7]. This feature would make
nanofluids very attractive coolant for high heat flux devices at elevated
temperatures. Das attributed this feature to the motion of nanoparticles. Some of
the studies focusing on the effect of particles size [8-10], particle shape on the
thermal conductivity of nanofluids [8, 11] and the aspect ratio of nanoparticles
[12]. The largest increase in thermal conductivity has been observed in
suspensions of carbon nanotubes (CNT). Carbon nanotubes consist of
nanosized tubular graphene sheet based material with high aspect ratio. It is one
of the most fascinating allotropes of carbon having simple chemical composition
but good mechanical strength with remarkable thermal and electrical properties
[13]. The first report on the synthesis of nanotubes was conducted by Iijima [14].
The effective thermal conductivity of multiwalled carbon nanotubes and-oil (αolefin) mixtures were investigated by Choi et al [15]. Results showed that the
3
measured thermal conductivity was greater than the theoretical predictions and
was nonlinear with increasing nanotube concentration.
Nanofluids have been produced by two techniques: two-step technique in
which dried nanoparticles have to be synthesized in the form of dry powders, and
then the particles are dispersed in the liquid and homogenized by ultrasonic
baths and magnetic stirrers. In order to form stable dispersion, surfactant is used
generally during the formulation to homogenize the suspensions. The other
technique is one-step technique; this process consists of simultaneously making
and dispersing the particles in the fluid. In this method, the processes of drying,
storage, transportation, and dispersion of nanoparticles are avoided, so the
agglomeration of nanoparticles is minimized, and the stability of fluids is
increased [16]. Several theories have been proposed to explain the anomalous
thermal conductivity behavior. The most prevalent theories involve the Brownian
motion of particles to create a microconvective effect, or the ordering of liquid
molecules at the solid interface to enhance conduction through those molecules,
or the clustering of nanoparticles to form pathways of lower thermal resistance.
Carbon nanotubes offer a possible solution to optimizing the cooling fluid
performance by adding both high surface area and high thermal conductivity
nanoadditives. A carbon nanotube can basically be thought of as a graphene
sheet rolled up to construct a hollow cylinder of carbon. The tubular form allows
for a very large surface area to volume ratio. The outer radius of this tube is
around 10 nm, and as a result of this incredibly small scale, carbon nanotubes
can exhibit high thermal conductivity value of 2000 w/m-K. Because of the
4
electron delocalization properties of the graphene sheets from which they are
constructed, the electrical and thermal conductivity along the tube wall is very
large. Conductivity properties in the direction perpendicular to this plane are very
poor. The goals of this project, therefore, are:
(1) To use small amount of carbon nanotubes in fluid with either surface
treatments through functionalization process or surfactant additives to
reach full dispersion of additives
(2) To test the thermal properties of the resulting material in both static and
dynamic modes.
(3) To use a numerical analysis in attempt to model at least the thermal
conductivity increase as a function of carbon nanotubes concentration
into a base fluids.
The following chapter (chapter 2) gives a comprehensive literature review of
thermal nanofluids, materials fabrication and various methods to measure
thermal conductivity of fluids in both static and dynamic modes. Chapter 3 deals
with a detailed description of materials fabrication, method of characterization
and thermal test system design and its components. It is also focuses on the
experimental analysis. Chapter 4 includes the formulation of a theoretical model
for the thermal performance of CNT nanofluid. Finally, chapter 5 provides with
conclusions from the present study and its implications on the ongoing and future
thermal management applications.
5
CHAPTER II
LITERATURE REVIEW
2.1. Introduction
Nanofluids are nanoscale colloidal suspensions containing nanometersized materials (nanoparticles, nanotubes, nanorods) with diameter sizes on the
order of 1 to 100 nanometers suspended in heat transfer base fluids. Nanofluids
have been found to possess enhanced thermophysical properties such as
thermal conductivity, thermal diffusivity, viscosity, and convective heat transfer
coefficients compared to those of base fluids like oil or water. [17] The thermal
conductivity of heat transfer fluids is important to determine the efficiency of heat
exchange systems. Since the size of heat exchange systems can be reduced
with the highly efficient heat transfer fluids, the enhancement of the thermal
conductivity will contribute to the miniature devices.
Several investigations have revealed that the thermal conductivity of the
fluid containing nanoparticles could be increased by more than 20% for the case
of very low nanoparticles concentrations. When nanofluids were explored by
Choi and his group at the Argonne National Laboratory, they first tried to use
oxide particles of nanometer size to suspend in the common coolants (e.g.,
water, ethylene glycol). [2]
6
All physical mechanisms have a critical length scale below which the
physical properties of materials are changed, therefore, particles smaller than
100 nm exhibit properties different from those of conventional solids. The
properties of nanophase materials come from the relatively high surface
area/volume ratio, which is due to the high proportion of constituent atoms
residing at the grain boundaries [35].
Production of nanoparticles materials used in nanofluids can be classified
into two main categories: physical processes and chemical processes. Typical
physical process includes the mechanical grinding method and the inert-gascondensation technique. Chemical process for producing nanoparticles include
chemical precipitation, spray pyrolysis, and thermal spraying. [50]
2.2. Nanofluids Preparation
2.2.1. One-Step Method
To reduce the agglomeration of nanoparticles, Eastman et al. developed a
one-step physical vapor condensation method to prepare Cu-ethylene glycol
nanofluids [5]. Liu et al. synthesized nanofluids containing Cu nanoparticles in
water through chemical reduction method. [23]
The one-step process consists
of simultaneously making and dispersing the particles in the fluid.
This method has some advantages, the processes of drying, storage,
transportation, and dispersion of nanoparticles are avoided, so the agglomeration
of nanoparticles is minimized, and the stability of fluids is increased, but a
7
disadvantage of this method is that only low vapor pressure fluids are compatible
with the process. This limits the application of the method [16]. One-step
processes can prepare uniformly dispersed nanoparticles, and the particles can
be stably suspended in the base fluid. [17]
2.2.2. Two-Step Method
This is the first and the most classic synthesis method of nanofluids, which
is extensively used in the synthesis of nanofluids considering the available
commercial nano-powders supplied by several companies. Nanoparticles,
nanotubes, nanotubes, or other nanomaterials used in this method are first
produced as dry powders by chemical or physical methods. Then, the nanosized
powder will be dispersed into a fluid in the second processing step with the help
of intensive magnetic force agitation, ultrasonic agitation, high-shear mixing, and
homogenizing. Two-step method is the most economic method to produce
nanofluids in large scale, because nanopowders synthesis techniques have
already been scaled up to industrial production levels. Due to the high surface
area and surface activity, nanoparticles have the tendency to aggregate. The
important technique to enhance the stability of nanoparticles in fluids is the use of
surfactants. [17]
2.2.3. Stability
The stability of nanofluids is very important in order for practical
applications. Stability of nanofluid is strongly affected by the characteristics of the
suspended particle and basefluid such as the particle morphology, the chemical
8
structure of the particles and basefluid. [38] Because of the attractive Van der
Waals forces between the particles, they tend to agglomerate before they are
dispersed in the liquid (especially if nanopowders are used); therefore, a means
of separating the particles is necessary. Groups of particles will settle out of the
liquid and decrease the conductivity of the nanofluid. Only by fully separating all
nanoparticle agglomerates into their individual particles in the host liquid will a
well-dispersed, stable suspension exist, and only under this condition will the
optimum thermal conductivity exist. [39]
Nanoparticles used in nanofluids have been made of various materials,
such as metals (Cu, Ag, Au), metals oxide (Al2O3, CuO), carbide ceramic (SiC,
TiC) and carbon nanotubes. Metal Oxides were tried mainly for ease of
manufacture and stabilization compared to pure metallic particles, which are
difficult to suspend without agglomeration. Subsequently, many investigators
carried out experiments with oxide particles, predominantly Al2O3 particles, as
well as CuO, TiO2, and stable compounds such as SiC [35].
Eastman et al. [18] stated that an aqueous nanofluid containing 5 %
volume fraction CuO nanoparticles exhibited a thermal conductivity 60 % greater
than that of water. Additionally, they reported 40 % greater thermal conductivity
compared to water for an aqueous nanofluid containing 5 % volume fraction of
Al2O3 nanoparticles [18]. Lee et al. [3] and Wang et al. showed that alumina and
copper oxide nanoparticles suspended in water and ethylene glycol significantly
enhance the fluid thermal conductivity [19].
9
Xie et al. [8] observed a 21% increase in fluid thermal conductivity of water
with 5 vol. % of alumina nanoparticle. Pang et al. [20] demonstrated 10.74%
improvement of the effective thermal conductivity at 0.5 vol. % of Al2O3
nanoparticles and 14.29% of SiO2 nanoparticles at the same concentration.
Particle loading would be the main parameter that influences the thermal
transport in nanofluids. Particle loading is a parameter that is investigated in
almost all of the experimental studies. Most of the nanofluid thermal conductivity
data in the literature exhibit a linear relationship with the volume fraction of
particles. [3] [5] [8] [77]. However, some exceptions have shown a non-linear
relationship [11] [24][76]. Kwak et al. [21] in their investigation on CuO – ethylene
glycol nanofluid, observed that substantial enhancement in thermal conductivity
with respect to particle concentration is attainable only when particle
concentration is below the dilute limit.
The particle material is an important parameter that affects the thermal
conductivity of nanofluids, it seems that better enhancement in thermal
conductivity can be achieved with high thermal conductivity particles. Eastman et
al [5] investigated thermal conductivity enhancement of nanofluid consisting of
copper nanometer-sized particles dispersed in ethylene glycol. The effective
thermal conductivity is shown to be increased by up to 40% at 0.3 vol. % of Cu
nanoparticles which is much higher than ethylene glycol containing the same
volume fraction of dispersed oxide nanoparticles. However, the thermal
conductivity of dispersed nanoparticles is not crucial to determine the thermal
conductivity of nanofluids; some research show that particle type may affect the
10
thermal conductivity of nanofluids in other ways. Hong et al. [24] reported an
interesting result that the thermal conductivity of Fe based nanofluid was higher
than the one obtained for Cu nanofluids of the same volume fraction. This is
opposite to the expectation that the dispersion of the higher thermal conductive
material is more effective in improving the thermal conductivity.
The intrinsic properties of nanoscaled materials become different from
those of the bulk materials due to the size confinement and surface effect . [24]
From the previous researches it is observed that with using the same particle
materials there are a discrepancy in the thermal conductivity results even in the
same particle load. This discrepancy is attributed to variety of physical and
chemical parameters, in addition to the volume fraction, and the species of the
nanoparticles, other parameters such as the size, the shape, pH value and
temperature of the fluids and the aggregation of the nanoparticles, have been
proposed to play roles on the heat transfer characteristics of nanofluids.
2.3. Effective Parameters on Thermal Conductivity
2.3.1. Particle Size
It is expected that the thermal conductivity enhancement increases with
decreasing the particles size, which leads to increasing the Specific Surface Area
(SSA):
SSA =
(2.1)
11
For the sphere particles:
SSA =
(2.2)
This is clearly indicates that a decrease in particle diameter ( dp) causes
the SSA to increase, which giving more heat transfer area between the particles
and fluid surrounding the particles. Lee et al. [3] suspended CuO and Al 2O3 (18.6
and 23.6 nm, 24.4 and 38.4 nm, respectively) in two different base fluids: water
and ethylene glycol (EG) and obtained four combinations of nanofluids: CuO in
water, CuO in EG, Al2O3 in water and Al2O3 in EG. Their results show that the
thermal conductivity ratios increase almost linearly with volume fraction. Results
suggest that not only particle shape but size is considered to be dominant in
enhancing the thermal conductivity of nanofluids.
Xie et al. [8] demonstrated the nanoparticle size effect on the thermal
conductivity enhancement; they measured the effective thermal conductivity of
nanofluids (Al2O3 in ethylene glycol) with different nanoparticle sizes. They
reported an almost linear increase in conductivity with the volume fraction, but
the rates of the enhanced ratios to the volume fraction depend on the dispersed
nanoparticles. They stated that the enhancements of the thermal conductivities
are dependent on Specific Surface Area (SSA) and the mean free path of
nanoparticles and the base fluid, as the particle size decreases, the Brownian
motion of nanoparticles is greater and then nanoconvection becomes dominant.
As a result, the effective thermal conductivity of nanofluids becomes larger.
12
Yoo et al [22] investigated the thermal conductivities of (TiO2, Al2O3, Fe, and
WO3) nanofluids. It seems that the surface-to-volume ratio of nanoparticles is a
key factor in determining thermal conductivity of nanofluids.
2.3.2. Particle Shape
Murshed et al. [11] measured the effective thermal conductivity of rodshapes (10 nm x 40 nm; diameter by length) and spherical shapes (15nm) of
TiO2 nanoparticles in deionized water. The results show that the cylindrical
particles present a higher enhancement which is consistent with theoretical
prediction, i.e., Hamilton-Crosser [36] model.
Evans et al. [12] investigated the effect of the aspect ratio on the
enhancement of the thermal conductivity of nanofluids. They compared the
thermal conductivity enhancement of long fibers and flat plates at specific
concentration for different aspect ratio. The results suggest that the optimum
design for nanofluids for thermal conductivity enhancement would involve the use
of high-aspect-ratio fibers, e.g. carbon nanotubes, rather than spherical or
ellipsoidal particles.
2.3.3. Base Fluid
Different types of fluids, such as water, ethylene glycol, vacuum pump oil
and engine oil, have been used as base fluid in nanofluids. It is clearly seen that
no matter what kind of nanoparticle was used, the thermal conductivity
enhancement decreases with an increase in the thermal conductivity of the base
fluid. [37] Xie et al. [8] investigated the thermal conductivity of suspensions
13
containing nanosized alumina particles, for the suspensions using the same
nanoparticles; the enhanced thermal conductivity ratio is reduced with increasing
thermal conductivity of the base fluid. Liu et al. [28] also investigated base fluid
effect with MWCNT nanofluids; they used ethylene glycol and synthetic engine oil
as base fluids in the experiments. 1 vol. % MWCNT/ethylene glycol nanofluid
showed 12.4% thermal conductivity enhancement, whereas for 2 vol. %
MWCNT/synthetic engine oil nanofluid, enhancement was 30%. It was observed
that higher enhancements were achieved with synthetic engine oil as the base
fluid.
2.3.4. Temperature-Dependent Thermal Conductivity
In conventional suspensions of solid particles (with sizes on the order of
millimeters or micrometers) in liquids, thermal conductivity of the mixture
depends on temperature only due to the dependence of thermal conductivity of
base liquid and solid particles on temperature.[48] However, in case of
nanofluids, change of temperature affects the Brownian motion of nanoparticles
and clustering of nanoparticles [96] which results in dramatic changes of thermal
conductivity of nanofluids with temperature.
Das et al. [10] discovered that nanofluids have strongly temperaturedependent conductivity compared to base fluids. They measured effective
thermal conductivities of Al2O3 and CuO nanoparticles in water when the mixture
temperature was varied between 21 to 51oC. It is observed that a 2 to 4 fold
increase in thermal conductivity enhancement of nanofluids can take place over
that range of temperature. Patel et al [97] reported that the thermal conductivity
14
enhancement ratios of Au nanofluids were enlarged considerably when the
temperature increased.
Zhang et al [61] measured effective thermal conductivity of Al 2O3-distilled
water in the temperature range 5-50o C, their results agree with the results of
[10]. However, in other experimental tests, it showed different thermal
conductivity enhancement behaviors, Yu et al., investigated nanofluids containing
GONs (Graphene Oxide Nanosheets), the thermal conductivity enhancement
ratios remain almost constant when the tested temperatures vary. This indicates
that many factors may affect the thermal conductivity enhancement ratios. One of
these factors may be the viscosities of the base fluids. In their experiments, EG
was used as the base fluid and the viscosity value was high. On the other hand,
GONs were large, so the effect of Brownian motion was not obvious. [98]
2.4. Mechanisms of Thermal Conduction Enhancement
Heat conduction mechanisms in nanofluids have been extensively
investigated in the past decades to explain the experimental observations the
enhanced thermal conductivity. Keblinski et al. [41] and Eastman et al. [42]
proposed four possible mechanisms, e.g., Brownian motion of the nanoparticles,
molecular-level layering of the liquid at the liquid/particle interface, the nature of
heat transport in the nanoparticles, and the effects of nanoparticle clustering.
Other groups have started from the nanostructure of nanofluids. These
investigators assume that the nanofluid is a composite, formed by the
nanoparticle as a core, and surrounded by a nanolayer as a shell, which in turn is
15
immersed in the base fluid, and from which a three-component medium theory
for a multiphase system is developed. [55]
2.4.1. The Brownian Motion
The Brownian motion of nanoparticles could contribute to the thermal
conduction enhancement through two ways, a direct contribution due to motion of
nanoparticles that transport heat, and an indirect contribution due to microconvection of fluid surrounding individual nanoparticles. The studies of Wang et
al. [19] clearly showed that Brownian motion is not a significant contributor to
heat conduction. Keblinski et al. [41] concluded that the movement of
nanoparticles due to Brownian motion was too slow in transporting heat through
a fluid. To travel from one point to another, a particle moves a large distance over
many different paths in order to reach a destination that may be of a short
distance from the starting point. Therefore, the random motion of particles cannot
be a key factor in the improvement of heat transfer based on the results of a
time- scale study. Evans et al. [43] suggested that the contribution of Brownian
motion to the thermal conductivity of the nanofluid is very small and cannot be
responsible for the extraordinary thermal transport properties of nanofluids.
Even though it had been stated that Brownian motion is not a significant
contributor to enhanced heat conduction, some authors show the key role of
Brownian motion in nanoparticles in enhancing the thermal conductivity of
nanofluids. [35] Jang and Choi [44] proposed the new concept that the
convection induced by purely Brownian motion of nanoparticles at the molecular
and nanoscale levels is a key nanoscale mechanism governing their thermal
16
behavior. In this mechanism, the thermal conductivity of nanofluids is strongly
dependant on temperature and particle size.
Patel et al. [45] developed microconvection model for evaluation of
thermal conductivity of nanofluid by taking into account nanoconvection induced
by Brownian nanoparticles and their specific surface area. Koo et al [46]
discussed the effects of Brownian, thermo-phoretic, and osmo-phoretic motions
on the effective thermal conductivities. They found that the role of Brownian
motion is much more important than the thermo-phoretic and osmo-phoretic
motions. Furthermore, the particle interaction can be neglected when the
nanofluid concentration is low (< 0.5%). The contribution of Brownian motion for
high aspect ratio nanotube dispersions may not be as important as that for
spherical particle dispersions, Xie et al. [25] pointed out that the thermal
conductivity of nanofluids seems to be very dependent on the interfacial layer
between the nanotube and base fluids in their experiments.
2.4.2. Molecular-level Layering
At the solid-liquid interface, liquid molecules could be significantly more ordered
than those in the bulk liquid. In the direction normal to the liquid–solid interface,
liquid density profiles exhibit oscillatory behavior on the molecular scale due to
interactions between the atoms in the liquid and the solid [50,47]. The magnitude
of the layering increases with increasing solid–liquid bonding strength, and the
layering extends into the liquid over several atomic or molecular distances. In
addition, with increasing strength of the liquid–solid bonding, crystal-like order
develops in the liquid in the lateral directions. [51] Therefore, Choi et al. postulate
17
that this organized solid/liquid interfacial shell makes the transport of energy
across the interface effective [15].
There is no experimental data regarding the thickness and thermal
conductivity of these nanolayers is an important drawback of the proposed
mechanism [48]. To develop a theoretical model by considering liquid layering
around nanoparticles some authors assumed some values for the thermal
conductivity and thickness of the nanolayer [52].
Recently, Tillman and Hill [53] proposed another theoretical way to
calculate the thickness and thermal conductivity of the nanolayer. Their approach
requires a prior assumption about the functional form of the thermal conductivity
in the nanolayer and iterations of the calculation process are required. They used
the classical heat conduction equation together with proper boundary conditions
to obtain a relation between the radial distribution of thermal conductivity in the
nanolayer and nanolayer thickness. Lee [54] proposed a way of calculating the
thickness and thermal conductivity of the nanolayer by considering the formation
of electric double layer around the nanoparticles. So, the thickness of nanolayer
depends on the dielectric constant, ionic strength, and temperature of the
nanofluid. For the thermal conductivity of the nanolayer, it is depends on the total
charged surface density, ion density in the electric double layer, pH value of the
nanofluid, and thermal conductivities of base fluid and nanoparticles. [55]
Xue [49] proposed a model of the effective thermal conductivity for
nanofluids considering the interface effect between the solid particles and the
base fluid in nanofluids, his model based on Maxwell theory and average
18
polarization theory. The theoretical results on the effective thermal conductivity of
nanotube/oil nanofluid and Al2O3/water nanofluid are in good agreement with the
experimental data.
Among those studies, Xue et al. [51] examined the effect of nanolayer by
molecular dynamics simulations and showed that nanolayers have no effect on
the thermal transport. Their explanation is that despite the large degree of
ordering these liquid layers are still more disordered than the crystal.
2.4.3. Clustering
Clustering is the formation of larger particles through aggregation of
nanoparticles. Clustering effect is always present in nanofluids and it is an
effective parameter in thermal conductivity. [48]
Figure (2-1) schematically
shows aggregation. The probability of aggregation increases with decreasing
particle size, at constant volume fraction, because the average interparticle
distance decreases, making the attractive van der Waals force more important.
[35] Aggregation will decrease the Brownian motion due to the increase in the
mass of the aggregates, whereas it can increase the thermal conductivity due to
percolation effects in the aggregates, as highly conducting particles touch each
other in the aggregate. [56] However, large clusters tend to settle out from the
base fluids and therefore decrease the thermal conductivity enhancement.
19
Aggregated Nanoparticles
High-Conductivity
Percolation Path
Figure (2-1) Schematic of Well-dispersed Aggregates.
A number of authors proposed that strongly suggest that nanoparticle
aggregation plays a significant role in the thermal transport in nanofluids. Using
effective medium theory Prasher et al. [57] demonstrate that the thermal
conductivity of nanofluids can be significantly enhanced by the aggregation of
nanoparticles into clusters. Hong et al. [58] investigated the effect of the
clustering of nanoparticles on the thermal conductivity of nanofluids. Large
enhancement of the thermal conductivity is observed in Fe nanofluids sonicated
with high powered pulses. The average size of the nanoclusters and thermal
conductivity of sonicated nanofluids are measured as time passes after the
sonication stopped. It is found from the variations of the nanocluster size and
thermal conductivity that the reduction of the thermal conductivity of nanofluids is
directly related to the agglomeration of nanoparticles. Kwak and Kim [21]
demonstrated that large thermal conductivity enhancements are accompanied by
sharp viscosity increases at low (<1%) nanoparticle volume fractions, this
confirms that it is more effective to use small volume fractions than otherwise, in
nanofluids. Lee et al. [59] demonstrated the critical importance of particle surface
20
charge in nanofluid thermal conductivity. The surface charge is one of the
primary factors controlling nanoparticle aggregation.
Furthermore, Putnam et al. [60] and Zhang et al [61] and Venerus et al.
[62] demonstrated that nanofluids exhibiting good dispersion do not show any
unusual enhancement of thermal conductivity. [12]
2.5. Carbon Nanotubes
The largest increases in thermal conductivity have been observed in
suspensions of carbon nanotubes (CNT), which have very high aspect ratio (~
2000), and very high thermal conductivity along their alignment axis, similar to
the in-plane conductivity of graphite (2000 W/m K); but the conductivity
perpendicular to the axis is probably similar to that for transplanar conduction in
graphite. The first report on the synthesis of nanotubes was conducted by Iijima
[14].
CNTs are one-dimensional cylinder of carbon with single or multiple layers
of carbon. A single sheet of graphite is called graphene. Graphene is a densely
packed single, hexagonal layer of carbon-bonded atoms that are rolled to form a
cylindrical microstructure. The ends of the cylindrical microstructure can be
capped with a hemispherical structure from the fullerene family or left open.
Planner carbon sheets can be rolled in a number of ways. The orientation of
rolling gives different possible structures of carbon nanotubes. Each type of CNT
structure has their unique strength, electrical and thermal properties. [35]
21
There are two main types of carbon nanotubes, single wall carbon
nanotubes (SWCNT) and multi-wall carbon nanotubes (MWCNT). SWCNTs are
composed of a single sheet of graphene rolled into a cylinder capped with onehalf of a fullerene molecule at each end of the cylinder. Figure (2-2) A MWCNT
consists of concentric sheets of rolled graphene that are either capped with onehalf a fullerene molecule at each end or left open.
MWCNT
SWCNT
Figure (2-2) SWCNT and MWCNT
Recent studies reveal that CNTs have unusually high thermal conductivity.
[78][79] It can be expected that the suspensions containing CNTs would have
enhanced thermal conductivity and their improved thermal performance would be
applied to energy systems.
The first experimental observation of thermal conductivity enhancement
was reported by Choi and co-workers for the case of MWNTs dispersed in poly(α olefin) oil [15]. They reported an enhancement of 160% at a nanotube loading
of 1.0 vol. %. To get stable nanofluids, Xie et al. [25] functionalized CNTs using
concentrated nitric acid which reduced aggregation and entanglement of the
CNTs. Functionalized CNTs were successfully dispersed into polar liquids like
22
distilled water, ethylene glycol without the need of surfactant and into non polar
fluid like decene (DE) with oleylamine as surfactant. Functionalized CNTs are
stable in both water and ethylene glycol for more than two months. At 1.0 vol. %
the thermal conductivity enhancements are 19.6%, 12.7%, and 7.0% for
functionalized CNT suspension in DE, EG, and DW, respectively.
It is known that CNTs have a hydrophobic surface, which is prone to
aggregation and precipitation in water in the absence of dispersant/surfactant
[40]. The method of making a stable suspension includes physical mixing in
combination with chemical treatments. The physical mixing includes magnetic
force agitation and ultrasonic vibration. The chemical treatment is achieved by
changing the pH value and by using surface activators and/or surfactant.
Various dispersion methods have been used to ensure a homogenous
dispersion of the CNTs throughout the nanofluid. [81] The surfactant using
includes two-steps approach: dissolving the surfactant into the liquid medium,
and then adding the selected carbon nanotube into the surfactant liquid medium
with mechanical agitation and/or ultrasonication [40] All these techniques used in
preparation process and the addition of surfactant, aim at changing the surface
properties of suspended particles and suppressing formation of particles
aggregation, so, nanofluids can keep stable without visible sedimentation of
nanoparticles. [4]
Assael et al. [26] measured the enhancement of the thermal conductivity
of MWCNTs-water suspensions with 0.1 wt% Sodium Dodeycyl Sulfate (SDS) as
a surfactant. The maximum thermal conductivity enhancement was 38% for a 0.6
23
vol. % suspension. Results showed that the additional SDS would interact with
MWCNTs in that the outer surface was affected. Later, Assael et al. [27]
repeated the similar measurements using MWCNTs and double walled carbon
nanotubes (DWNTs), but using Hexadecyltrimethyl ammonium bromide (CTAB)
and nanosphere AQ as dispersants. The maximum thermal conductivity
enhancement obtained was 34% for a 0.6 vol. % MWCNTs –water suspension
with CTAB. They also discussed the effect of surfactant concentration on the
effective thermal conductivity of the suspensions and found that CTAB is better
for MWCNTs and DWNTs.
Liu et al. [28] tested nanofluids containing CNTs. They used ethylene
glycol and synthetic engine oil as base fluids. N-hydroxysuccinimide (NHS) was
employed
as
the
dispersant
in
carbon
nanotube–synthetic
engine
oil
suspensions. It was found that in CNT ethylene glycol nanofluids, the thermal
conductivity was enhanced by 12.4% with 1 vol. % CNT, while in CNT engine oil
nanofluids; the thermal conductivity was enhanced by 30.3% with 2 vol. % CNT.
Based on experimental observations of CNT–liquid and CNT–CNT interactions,
CNT dispersed in base fluid; where CNT orientation and CNT–CNT contacts, can
form extensive three-dimensional CNT network chain that facilitate thermal
transport [34].
Nanda et al. [29] reported up to 35% enhancement in thermal conductivity
for 1.1 vol. % CNTs (single wall) glycol nanofluid. Shaikh et al. [30] used the
modern light flash technique and measured the thermal conductivity of three
types of nanofluids. They reported a maximum enhancement of 160% for the
24
thermal
conductivity
of
carbon
nanotube
(CNT)-polyalphaolefin
(PAO)
suspensions. Ding et al. [31] studied the heat transfer behavior of aqueous
suspensions of multi-walled carbon nanotubes flowing through a horizontal tube.
Wen and Ding found a 25% enhancement in the conductivity carbon nanotubes
suspended in water. The enhancement in the conductivity of the suspension
increases rapidly with loading up to 0.2 vol. % and then begins to saturate, the
measurements are taken up to 0.8 vol. [80]
Significant enhancement of the convective heat transfer is observed and
the enhancement depends on the flow conditions and CNT concentration. They
found that the enhancement is a function of the axial distance from the inlet of
the test section and they proposed that particle re-arrangement, shear induced
thermal conduction enhancement, reduction of thermal boundary layer thickness
due to the presence of nanoparticles, as well as the very high aspect ratio of
CNTs are to be possible mechanisms. Jiang et al. [32] tested the thermal
characteristics of CNT nanorefrigerants, four kinds of CNTs employed in this
research with different diameters and aspect ratio. The experimental results
show that the thermal conductivities of CNT nanorefrigerants increase
significantly with the increase of the CNT volume fraction; the diameter and
aspect ratio of CNT can influence the thermal conductivities of CNT
nanorefrigerants, in which the smaller the diameter of CNT is or the larger the
aspect ratio of CNT is, the higher the thermal conductivity of CNT nanorefrigerant
is. They reported that the influence of aspect ratio of CNT on nanorefrigerants’
thermal conductivities is less than the influence of diameter of CNT.
25
Meibodi et al. [33] investigated the stability and thermal conductivity of
CNT/water nanofluids. They examined the affecting parameters including size,
shape, and source of nanoparticles, surfactants, power of ultrasonic, time of
ultrasonication, elapsed time after ultrasonication, pH, temperature, particle
concentration, and surfactant concentration. The work on CNTs containing
nanofluids that are cited above clearly indicates that nanotubes have a higher
potential to be used in nanofluids.
2.6. Modeling Studies
The conventional understanding of the effective thermal conductivity of
multiphase systems originates from continuum formulations which typically
involve the particle size/shape and volume fraction and assume diffusive heat
transfer in both fluid and solid phases. Researchers have proposed many
theories to explain the anomalous behavior observed in nanofluids. First attempt
to explain the thermal improvement in nanofluids was made using Maxwell theory
[1]. This theory is valid for diluted suspension of spherical particles in
homogeneous isotropic material.
=
(2.3)
The Maxwell equation takes into account only the particle volume
concentration and the thermal conductivities of particle and liquid. Hamilton and
Crosser [36] developed this theory for non spherical particles shapes their model
allows calculation of the effective thermal conductivity (keff) of two component
26
heterogeneous mixtures and includes empirical shape factor n given by n=3/ψ;
(ψ is the sphericity defined as ratio between the surface area of the sphere and
the surface area of the real particle with equal volumes),
=
(2.4)
Where kp and k0 are the conductivities of the particle material and the
base fluid and ϕ is volume fraction of nanoparticles Hamilton-Crosser theory
takes into account the increase in surface area of the particles by taking the
shape factor into account, but it does not consider the size of the particles. This
is an obvious shortcoming of this theory. It was not surprising that both Maxwell’s
theory and HC theory were not able to predict the enhancement in thermal
conductivity of nanofluids because it did not take into account the various
important parameters affecting the heat transport in nanofluids like the effect of
size of nanoparticle and modes of thermal transport in nanostructures. Other
classical models include the effects of particle distribution Cheng & Vachon [63],
and particle/particle interaction Jeffrey [64]. Although they can give good
predictions for micrometer or larger-size multiphase systems, the classical
models usually underestimate the thermal conductivity increase of nanofluids as
a function of volume fraction.
Keblinski et al. [41] investigated the possible factors of increasing thermal
conductivity in nanofluids such as the size, the clustering of particles, Brownian
motion of particles and the nanolayer between the nanoparticles and base fluids.
27
Yu and Choi [52] proposed a modified Maxwell model to account for the effect of
the nanolayer by replacing the thermal conductivity of solid particles kp in Eq.(1)
with the modified thermal conductivity of particles kpe, which is based on the socalled effective medium theory developed Schwartz et al.[65];
=
Where
(2.5)
is the ratio of nanolayer thermal conductivity to
particle thermal conductivity and β =
is the ratio of the nanolayer thickness to
the original particle radius. This model can predict that the presence of very thin
nanolayer, even though only a few nanometers thick, can measurably increase
effective volume fraction and subsequently the thermal conductivity of nanofluids.
Xue [66] proposed a model for calculating the effective thermal
conductivity of nanofluids, which is expressed as
…. (2.6)
where ke,x and ke,y the thermal conductivity components of the complex elliptical
particle along the x and y axes, respectively, ν and
are the volume fractions of
nanoparticles and complex nanoparticles (nanoparticle with interfacial shell),
28
respectively. His model is based on the Maxwell theory and average polarization
theory, which includes the interfacial shell effect.
Shukla et al. [67] developed a model for thermal conductivity of nanofluids
based on the theory of Brownian motion of particles in a homogeneous liquid
combined with the macroscopic Hamilton- Crosser model and predicted that the
thermal conductivity will depend on the temperature and particle size. The model
predicts a linear dependence of the increase in thermal conductivity of nanofluid
with the volume fraction of solid nanoparticles.
Xue and Xu [68] derive an expression for the effective thermal conductivity
of nanofluids with interfacial shells, the expression is not only depended on the
thermal conductivity of the solid and liquid and their relative volume fraction, but
also depended on the particle size and interfacial properties.
Patel et al. [69] proposed a cell model to predict the thermal conductivity
enhancement of nanofluids. Effects due to the high specific surface area of the
mono-dispersed nanoparticles (in which, inter-particle interactions are neglected,
as the particle–fluid heat transfer is expected to be much more significant as
compared to particle–particle heat transfer), and the micro-convective heat
transfer enhancement associated with the Brownian motion of particles are
addressed in this model. It is assumed in this model that there are two path for
the heat flux, one corresponding to the heat conduction directly through the
stationary liquid without involving the particle phase and the other in which heat
passes from the liquid to the moving particle, propagates by conduction within
the particle and finally returns to the liquid from particle phase, the convective
29
resistance between the fluid and the particle due to particle motion and the
conductive resistance through the particle are in series.
Akbari et al. [70] proposed an expression for the effective conductivity of
nanofluids; this model is based on Nan et al. model [71]. It takes into
consideration micro-convection between the liquid and the nanoparticles due to
Brownian motion, the effect of particle clustering size and the effects of the
interfacial thermal resistance.
In the last few years, attempts have been made to model the
enhancement in thermal conductivity of CNT nanofluids using liquid layering
scenario, fractal theory etc. Xue [72] modeled the thermal conductivity of CNT
nanofluids using field factor `approach, with a depolarization factor and an
effective dielectric constant. Hosseini et al. [73] uses a set of dimensionless
groups based upon the properties of the base fluid, the CNT-fluid interface, and
characteristics of the nanotubes themselves, such as diameter, aspect ratio, and
thermal conductivity. Patel et al. model [74] is derived from Hemanth et al [75],
which is given for nanoparticle suspensions. The model considers two paths for
heat to flow in a CNT nanofluid, one through the base liquid and the other one
through the CNTs. These two paths are assumed to be in parallel to each other.
Thus, two continuous media are considered here, participating in the conductive
heat transfer. Usually, the aspect ratio of CNTs is very high and hence, a
continuous net of CNTs available for heat transfer is a valid assumption.
30
2.7. Measurement of Thermal Conductivity of Liquids
There are three main methods commonly employed to measure the
thermal conductivity of nanofluids: The transient hot wire method, the steadystate parallel plate and temperature oscillation.
2.7.1. Transient Hot Wire Method (THW)
In the ideal mode of the transient hot-wire apparatus, an infinitely long,
vertical, line source of heat possessing zero heat capacity and infinite thermal
conductivity is immersed in a sample fluid whose thermal conductivity is to be
measured [83]. The hot wire served both as a heating unit and as an electrical
resistance thermometer. In practice, the ideal case is approximated with a finite
long wire embedded in a finite medium. Because in general nanofluids are
electrically conductive, a modified hot-wire cell and electrical system was
proposed by Nagasaka et al. [84] by coating the hot wire (typically platinum) with
an epoxy adhesive which has excellent electrical insulation and heat conduction.
The wire is electrically heated, and the rise in temperature over the time
elapsed is measured. Since the wire is essentially wrapped in the liquid, the heat
generated will be diffused into the liquid. The higher the thermal conductivity of
the surrounding liquid, the lower the rise in temperature will be. To calculate the
thermal conductivity of the surrounding liquid, a derivation of Fourier’s law for
radial transient heat conduction is used [35]. The differential equation for the
conduction of heat is
31
(2.6)
Using a solution presented by Carslaw and Jaeger [85], the conductivity of
a solution can be expressed as
(2.7)
where T1 and T2 represent the temperature of the heat source at time t 1 and t2
respectively.
The transient hot wire method has been used widely to measure the
thermal conductivities of nanofluids, however, Das et al. [10] pointed that
possible concentration of ions of the conducting fluids around the hot wire may
affect the accuracy of such experimental results.
2.7.2. Steady-State Parallel-Plate Method
This
method
produces
the
thermal
conductivity
data
from
the
measurement in a straightforward manner, the fluid sample is placed in the
volume between two parallel rounds copper plates figure (2-3). The two copper
plates are separated by small spacers with a specific thickness. There are two
heaters, one for upper plate which generates a heat flux to the lower plate and
one for the lower plate so as to maintain the uniformity of the temperature in the
lower plate. And there are heaters surrounding the whole system to eliminate the
convection and radiation losses from the upper and lower plates. The heat
supplied by the upper heater flows through the liquid between the upper and the
32
lower copper plates. Therefore, the overall thermal conductivity across the two
copper plates, including the effect of the spacers, can be calculated from the
one-dimensional heat conduction equation. [19] The disadvantages of steadystate methods are that heat lost cannot be quantified and may give considerable
inaccuracy, and natural convection may set in, which gives higher apparent
values of conductivity.
Figure (2-3) Steady State Parallel-plate Setup
2.7.3. Temperature Oscillation Method
The apparatus used in this method figure (2-4), consists of a hollow,
insulating cylinder of which central hole is closed from both the sides (surface A
and B) by two metal discs, leaving a central cylindrical (C) cavity available for test
fluid. At the surfaces A and B, periodic temperature oscillations are generated
with a specific angular velocity. Analytical solution for temperature distribution for
one dimensional, transient heat conduction with periodic boundary condition is
used to find the thermal conductivity of test fluid by measuring amplitude
33
attenuation and phase shift in the temperature wave at the inner face of the disc
and at the centre of cavity. [87] The details of the technique are given by Das et
al. [10]
Figure (2-4) Temperature Oscillation Setup
2.8 Dynamic Thermal Test
2.8.1. Shear Flow Test
The primary interest in nanofluids is the possibility of using these fluids for
heat transfer purposes. So, the nanofluids are expected to be used under flow
condition. To know how these fluids will behave, more studies on its flow and
heat transfer feature are needed. Shear rate is a parameter can affect the
thermal conductivity of nanofluid. Lee and Irvine investigated the effect of shear
rate on the thermal conductivity of Non-Newtonian fluids [88]. They found that
increasing the shear rate increased the thermal conductivity of the fluid. Shin
34
and Lee measured the thermal conductivity of suspensions containing micro
particles (20-300μm) under Couette flow [89]. This study also showed that
increasing the shear rate increased the thermal conductivity of the fluids.
Although neither of these studies used nanofluids, they are still relevant. Any
practical application of nanofluids would subject them to shear. Understanding
how they perform under shear, is therefore critical. A setup similar to that used
by Shin and Lee was used in this study to determine the effects of shear on
nanofluids.
2.8.2. Pipe Flow Test
Convective heat transfer refers to heat transfer between a fluid and a
surface due to the microscopic motion of the fluid relative to the surface. The
effectiveness of heat transfer is described by the heat transfer Coefficient, h,
which is a function of a number of thermo-physical properties of the heat transfer
fluids, the most significant ones are thermal conductivity, k, heat capacity, Cp,
viscosity, μ, density, ρ, and surface tension, σ. Although, measuring the thermal
conductivity gives an idea of how effective a nanofluid is as a thermal fluid, it
does not show the entire picture. Recently, more tests have been performed to
see how nanofluids perform in a convective situation. This is a more telling test
because it is much more similar to practical applications, and shows what effects
particle size and viscosity might have on a nanofluid’s performance. The majority
of these studies have used a pipe flow set up [31, 90-95]. These studies agreed
that nanofluids heat transfer coefficient had significantly improved.
35
Anoop et al. [91] found 25% increase in alumina nanofluids, with even
greater increases in the entrance region.
However, an increase in thermal
conductivity does not necessarily imply an increase in heat transfer coefficient.
Heris [93] found that copper based nanofluids showed an increase in thermal
conductivity. However, aluminum based nanofluids showed greater increases in
heat transfer coefficient. The authors suggested that the larger particle size and
larger viscosity of the copper fluids caused them to perform worse in convection
tests. Although these anomalies have been reported by others, there has been
far too little testing done to offer conclusive explanations why some nanofluids
offer small improvement in convection than in conduction.
That is the focus of this study. Three types of tests were performed on
three types of nanofluids.
Static, dynamic shear and pipe flow tests were
performed to obtain thermal conductivity data in static and dynamic scenarios
and heat transfer coefficient data.
36
CHAPTER III
MATERIALS AND CHARACTERIZATION METHODS
3.1. Materials Preparation
In this study nanotubes of the Pyrograf III family were heat treated at
different temperature to study the influence of crystallinity on the overall thermal
conductivity of nanofluids. The thermal and electrical properties of the resulting
carbon based nanofluids were analyzed using a variety of tests to determine the
effects of incorporating the carbon nanotubes into the fluids. The goal of the
investigation is to maximize the improvement to the physical properties of the
base fluid by determining the effect of various nanotubes crystallinity by
increasing treatments.
3.2. Electron Microscopy
High-resolution Scanning Electron Microscope (SEM) and Transmission Electron
Microscope (TEM) provide an analysis of the structural properties of the carbon
nanotubes and nanocomposites. These techniques are utilized to analyze the
effects of surface treatment on the pristine carbon nanotubes.
37
An emphasis was also placed on the nanocomposite fracture surface.
The use
of the grey-scale at this location provides a more thorough explanation of the
interaction between the nanotubes and the surrounding resin matrix.
Image contrast is obtained in electron microscopy by two phenomena.
Mass thickness contrast reflects differences in thickness, density, and the degree
of scattering of the specimen. The adsorption of the electron beam into the
specimen is quite small. A bright field image can be obtained when the aperture
in the back focal plane of the objective lens is small enough to eliminate
undesirable beams, but large enough to allow transmitted electron beams to
pass through. Conversely, in the dark field image, the incident beam must be
tilted for the hkl planes to be brought to the Bragg angle. The region emitting the
hkl beam will appear bright on a dark field. The observation of the specific lattice
plane extracted from the hkl planes facing arbitrary directions is possible by
changing the tilt angle and the aperture position. However, the dark field electron
microscope technique allows a complete exploration of the reciprocal space of
distorted materials like turbostratic carbons
If the aperture is reduced in size, the contrast improves while the resolution
deteriorates. This is why the resolution in the dark and bright field images is limited
to 3nm. When high resolution is required, the aperture must be opened widely so
that both the diffraction electron beams and the transmitted beams are collected to
give a lattice fringe image.
This describes the second contrast in electron
microscopy, the phase contrast. High-resolution fringe imaging clearly distinguishes
between graphitizable and non-graphitizable isotropic carbons with a random
38
arrangement of constituent lamellae. Difficulties in interpretation owing to the
influence of electron optical aberrations on the image-forming process may lead to
difficulties in terms of quantitative interpretation.
3.3. Raman Spectroscopy and X-rays
In addition to studying surface roughness, crystallinity was a very important
property to investigate because of how greatly each of the materials varied in
crystallinity. Past studies have not been able to truly capture how the molecular
structure of carbon nanostructures influences the nanofluids. Crystallinity was
explored using two different techniques.
Initially a Raman spectrometer was
used to study the crystallinity of each type of fiber, specifically the fiber’s average
crystal diameter, (La).
To help verify the results gathered from the Raman
spectrometer, an X-Ray diffractometer was used to calculate the crystallinity of
each material. The diffractometer operated at 40V and 30μA, and an X-Ray
wavelength of λ=0.154059. Once the material had been scanned, a variation of
the Scherrer equation for X-ray diffraction was used to calculate the components
of crystallinity within each material. The first variation of this equation was used
to calculate the average crystal heights (Lc) of the crystallites on each fiber:
(3.1)
where Lc : Average Crystallite Height, λ: X-ray wavelength for Carbon.
C: The line broadening at half the maximum intensity, θ: Bragg angle.
39
The 0.9 factor in the above equation and the 1.84 in the succeeding
equation represent the shape factor. A shape factor is used in x-ray diffraction
and crystallography to correlate the size of crystallites in a solid to the
broadening of a peak in a diffraction pattern. For the average diameter of the
crystallites of a fiber, La, the following variation of the Scherrer equation was
utilized:
(3.2)
where La : Average Crystallite Height, λ: X-ray wavelength for Carbon.
B: The line broadening at half the maximum intensity, θ: Bragg angle.
Because the Scherrer equation is most accurately applicable to nanosized particles, it deemed most appropriate for this particular application. From
the X-ray scan, all of the necessary parameters were obtained and recorded to
calculate both Lc and La for all three types of carbon nanotubes.
3.4. Electrical Analysis
The electrical properties of the carbon nanocomposites were studied
utilizing a four-point test according to ASTM B 193-87. The two outer leads of
the tester are connected to the current source and the two inner leads are used
to measure the voltage drop through the nanocomposite. Ohm’s law, equation
(3.3), allows for the resistance of the sample to be determined. By coupling the
calculated resistance with the known cross-sectional area (A) and distance
40
between leads (L) the resistivity (ρ) of the sample can be deduced as in equation
(3.4). Electrical conductivity (σ) is the reciprocal of resistivity and is calculated
according to equation (3.5).
(3.3)
where V is equal to voltage, I is the current and R is the nanocomposite
resistance.
(3.4)
(3.5)
3.5. Volume Resistivity
The volume resistivity of the carbon nanotubes was tested according to
ASTM D 257. The fixture used to complete the testing is shown in Figure (3-1).
The fixture was filled with the fiber sample, and the weight was recorded. The
tamper was inserted into the sample and an appropriate weight to create a
pressure of 16 pounds per square inch was placed on top of the tamper. The
distance between the top of the fixture and the tamper was measured and
recorded, and the resistance was measured using an ohm-meter.
41
Apply appropriate force
prior to measurements
Measure thickness of
sample
Teflon Sleeve
Measure resistance
between these two points
Nanotube
Aluminum Fixture
Figure (3-1) Fixture for Testing CNTs Volume Resistivity
Following the appropriate measurements at 16 pounds per square inch,
the procedure was repeated with an appropriate weight to create a pressure of
108 pounds per square inch.
. The volume resistivity is calculated as follows:
(3.6)
where (A) is the effective area of the measuring electrode, (t) is the sample
thickness and (Rv ) is the measured resistance.
42
3.6. Preparation of Nanofluids
3.6.1. Static and Dynamic
The nanofluids used for the static and dynamic tests were produced by
dispersing nanotubes into the base fluid. The same base fluid was used for all
samples. It contained ethylene glycol (EG) and a surfactant.
The ethylene glycol was 99.9% pure. The surfactant was solvent Naphtha.
Each sample of base fluid was formed by the addition of 1ml of the surfactant to
500ml of EG and was mixed by a magnetic stirrer for 30 minutes. The nanotubes
used in this project were AG, LHT, and HHT carbon nanotubes. Each type of
nanotube was dispersed into the base fluid with different mass fractions ranging
from 0.2% to 1.2% with a 0.2% increment. First, approximately 55mL of base
fluid was poured into a 100 mL beaker. This was massed and then the proper
mass of nanotubes was calculated, massed, and added to the base fluid. In
order to obtain a homogeneous and stable solution, two procedures were
adopted for mixing the fluid. The fluid was first mixed using a magnetic stirrer for
10 min and was then placed in an ultrasonic water bath for 30 min. It was
observed that after being sonicated the nanofluids were extremely stable and no
aggregation occurred. The use of mechanical stirring and ultrasonication broke
down the agglomerates and prevented the re-aggregation of nanotubes.
43
3.6.2. Pipe Flow
The pipe flow test setup required a minimum of 1500mL of fluid. Due to
the larger quantity needed, the procedure to produce the sample nanofluid
differed slightly from the one used to produce the samples for the static and
dynamic shear tests.
The procedure was largely the same with two major
differences. Instead of making a new solution for each concentration, the same
fluid was used. After tests were completed the mass of nanotubes needed to
make the next concentration was calculated then added, and mixed as before.
So first the base fluid was tested, then 0.2 wt% of nanotubes was added, then
after that was tested nanotubes were added to make it 0.4 wt%. This process
was continued until 0.8 wt% or 1.0 wt% was made. The other difference was in
how the nanofluid was mixed. Usually it was initially mixed by the magnetic
stirrer as before. However, sometimes it was too viscous, so it had to be stirred
by hand. It was then sonicated for approximately 30 minutes. Since two beakers
had to be used due to size constraints, the solutions had to be mixed back
together. They were poured into a 2000mL beaker and then mixed for
approximately 45 minutes using a “propeller mixer”.
44
3.7. Experimental Setup and Procedures
3.7.1. Static Test
Each of the nanofluids as well as the base fluid was tested under static
conditions to determine its conductivity. This gave data on how the nanotubes
affected the conductivity of the base fluid. It also provided a basis of comparison
for the dynamic data.
This was necessary so that a correlation between
conductivity under static and under dynamic conditions could be made. The
transient hot wire (THW) method was used to determine the conductivity of each
sample.
3.7.1.1. Static Test Apparatus
A KD2 Pro thermal property analyzer was used to perform the THW
conductivity tests.
The KD2 Pro took measurements every second for 90
seconds. This consisted of 30 seconds of equilibrium time, 30 seconds of heat
time, and 30 seconds of cool time. The data points were then automatically fit to
determine the thermal conductivity of the test specimen with an accuracy of 5%.
Each test took approximately two minutes, allowing for multiple tests to
be performed easily.
The instrument is very sensitive to slight temperature
changes. Therefore, an EchoTherm IC25XT chilling/heating dry bath was used to
maintain the samples at a constant 30˚C throughout the test. Figure (3-2) shows
A KD2 Pro thermal property analyzer and the EchoTherm IC25XT chilling/heating
dry bath. The samples were in glass vials that fit snuggly into the dry bath and
45
had a hole in their cap to allow the probe from the KD2Pro to be placed securely
into the sample figure (3-3).
Figure (3-2) KD2 Pro Thermal Property Analyzer (right) and EchoTherm
IC25XT Chilling/Heating Dry Bath (left)
Figure (3-3) The Probe of the KD2P (left) and the Probe is Placed into the
Sample Glass Vial (right)
46
3.7.1.2. Static Test Procedure
All samples were sonicated for approximately 20 minutes before being
poured into the vials. This ensured that the nanotubes were well distributed.
Before each test the vial was agitated to ensure that no settling had occurred.
The KD2 Pro probe was then inserted and the vial and probe were placed in the
dry bath. A reading was immediately taken. The KD2 Pro displayed conductivity
and temperature which was recorded.
A reading was then taken every two
minutes until the conductivity value stopped changing.
This usually took 20
minutes. Once a steady conductivity was reached the time between readings
was increased. First to 5 minutes for 2 or 3 reading, then to 10 minutes for 2
readings, finally to 15 minutes for a reading. This gave conductivity versus time
plot for each sample. Each set of tests took between 60 and 90 minutes. The
steady state conductivity value of each fluid was compared to the steady state
conductivity of the base fluid to determine the percent change in conductivity.
These results are presented and discussed in the following sections.
3.7.2. Dynamic Shear Test
The dynamic shear test was used to determine the effects of shear-rate on
a nanofluid’s thermal resistance.
This was used to determine the thermal
conductivity of the fluid under dynamic conditions. This data was compared to
the thermal conductivities measured under static conditions.
The thermal
resistance measurements were conducted under rotating Couette flow conditions
with a varying rotational speed of the outer cylinder.
47
3.7.2.1. Dynamic Shear Test Apparatus
A schematic illustration of the dynamic shear test section is shown in
Figure (3-4). The entire system is shown in Figure (3-5). The apparatus consist of
a rotating mechanism, a constant-temperature water bath, and measurement
devices. Thermal resistance was measured over a range of uniform shear rates.
The rotating mechanism consisted of a coaxial cylinder system in which the inner
cylinder was stationary and the outer cylinder was rotating. The inner cylinder
was made of aluminum with a 57.9 mm outside diameter, 67 mm length, and
3.3mm thickness. The outer cylinder was made of copper with a 63.5 mm outside
diameter, 110mm length, and 1 mm thickness. The test fluid was located in the
annular gap of 1.9 mm between the two cylinders. The gap was sufficiently small
compared to the radius of the inner cylinder. So, a plane Couette flow was
established between the two cylinders. Figure (3-6) shows the dynamic shear
test apparatus.
48
Figure (3-4) Schematic Illustration of the Dynamic Shear Test Section
Figure (3-5) The Entire System
49
Rotating Part
Control Unit
Heater Power Supply
Water bath
Figure (3-6) The Dynamic Shear Test Apparatus.
The inner cylinder consisted of three thermal probes containing a heater.
Heat flowed in a radial direction through the test fluid medium in the gap to the
outer cylinder. The coaxial cylinder system was placed in a constant-temperature
water bath to keep the outer cylinder at a constant temperature.
The
temperature of the outer cylinder can be assumed to be the same as that of the
water bath when the convective heat transfer coefficient on the surface of the
outer cylinder is significantly large. The performance of the water bath was fully
examined at different rotating speeds of the outer cylinder. Three thermocouples
were installed on the surface of the outer cylinder while stationary to measure the
50
wall temperature. This temperature was compared to the temperature of the
circulating water. The temperature difference was less than 0.5°C. This result
gives a relative error of less than 1% in the final thermal conductivity calculation.
Thus, the assumption that the surface temperature of the outer cylinder is the
same as the circulating water was supported. The assumption is even more
satisfactory with increasing the rotational speed, since the heat transfer rate
increases with increasing rotational speed, until the temperature difference
becomes negligible. For the inner cylinder, temperatures were measured using
three calibrated thermocouples. The thermocouples were inserted into the holes
in the inner cylinder wall at three axial locations of three different depths but at
the same radial position to determine the axial temperature distribution in the wall
of the inner cylinder.
3.7.2.2. Dynamic Shear Test Procedure
First, 32 mL of the sample nanofluid were poured into the outer cylinder of
the apparatus. After it was ensured that the proper volume of fluid was in the
cylinder, it was tightened into place.
Then the data acquisition system was
started and the heater was turned on. Using a variable AC transformer, the
supply voltage was adjusted until the thermocouples averaged 30°C.
This
voltage was maintained as the test went through 7 different shear rates (0, 150,
200, 300, 400, 500, and 600). The cylinder was rotated at a particular shear rate
until the supply voltage, temperature and shear rate did not change for several
minutes. Once this steady state was maintained, the test proceeded to the next
51
shear rate. This temperature, supply voltage, and resistance of the heaters were
used to compute the thermal resistance across the gap, due to the thermal
conductivity of the nanofluid being tested. This data is presented and discussed
in the following sections.
3.7.3. Pipe Flow Test
All of the nanofluids were tested using the pipe flow setup. This was done
to determine the effectiveness of the fluids in convective cooling.
The tests
showed how other factors such as viscosity affect the use of a particular
nanofluid as a coolant.
It also allowed connections between change in
conductivity and change in heat transfer coefficient to be made.
3.7.3.1. Pipe Flow Apparatus
The test apparatus consisted of a recirculating fluid loop which included a
pump, heated and instrumented test section, and a heat exchanger. The
complete test apparatus is shown in Figure (3-7). The fluid was pulled from the
reservoir by the diaphragm pump and went through a T connection. One stem
went through the test section and the other went back into the reservoir. The
flow rate through the test section was controlled by using the valves on each
stem of the T connection. The fluid then flowed through the heated pipe section.
Thermocouples were fitted to allow the temperature of the pipe to be recorded.
After leaving the pipe, the fluid passed through a heat exchanger, which cooled
the fluid before it was returned to the reservoir.
52
Inlet
Thermocouple
Recirculating
Chiller
Pump
Inside
Thermocouple
Fluid
Reservoir
Outside
Thermocouple
Heat
Exchanger
Outlet
Thermocouple
Shunt
Heated and Resistor Variable AC
Power
Insulated
Supply
Pipe
Figure (3-7) Nanofluid Pipe Flow Test Setup
The test section was fabricated from a length of copper pipe with outside
diameter of 0.375 inches and inside diameter of 0.311 inches. Thermocouples
were placed within the fluid stream (inside the pipe) and on the wall of the pipe
itself. Figure (3-8) shows both the inside and outside thermocouples. The copper
pipe was drilled and stainless steel sheathed thermocouples were soldered into
the holes to allow measurement of the fluid temperature. The thermocouples
were spaced every 6” along the heated length of the pipe. The outside
thermocouples were self-adhesive and were designed for surface temperature
measurement. Fiberglass tape was placed over the surface thermocouples to
prevent direct heating from the heater. This fiberglass tape can be seen between
the thermocouple and heater in Figure (3-9). Inlet and outlet thermocouples were
also installed to measure the overall temperature increase of the fluid as it flowed
through the pipe. The inlet and outlet thermocouples were separated from the
pipe by approximately 2 inches of tubing. This was done so that heat from the
53
heater would not conduct through the pipe and affect the reading of the
thermocouples. They were placed in a T connection using compression fittings.
The inlet and outlet thermocouple installation is shown in Figure (3-10).
Thermocouple Soldered into Tube
Self-Adhesive Thermocouple on Exterior
Figure (3-8) Inside and Outside Thermocouple Installation
Flexible heater uniformly wrapped
around entire length of tube
Figure (3-9) Test Section Just Prior to Wrapping with Insulation.
54
Inlet/Outlet
Thermocouple
Arrangement
Figure (3-10) Inlet and Outlet Thermocouple Installation.
The pipe was heated using a pair of flexible rope heaters wired in parallel
and wound helically around the pipe. The pipe was insulated using fiberglass
insulation wrap, which was then covered with polyethylene foam insulation. The
heaters, shown in Figure (3-9), were capable of supplying a nominal power of
600 watts to the pipe. A variable AC transformer was used to adjust the power
output of the heater. A shunt resistor was placed in series with the heaters to
facilitate current measurement and extra leads were attached to measure the
voltage across the heater as well. The AC transformer, shunt resistor and voltage
leads are shown in Figure (3-11).
55
Variable AC
Transformer
Shunt Resistor for
Current Measurement
Figure (3-11) Heater Power Source and Voltage/Current Measuring Leads
A heat exchanger was used to cool the fluid after it travelled through the
pipe. The heat exchanger consisted of two concentric copper coils. The outer coil
was connected to the chiller, which recirculated cold water through the copper
coil. The inner coil carried the sample nanofluid. The two coils were wrapped in
copper wire and soldered together in order to maximize heat transfer between
the two coils.
The coils were submerged in a bucket of water, which was
agitated by an aquarium pump. This provided some additional thermal mass to
smooth out the temperature of the cooling circuit and to provide better heat
transfer between the two copper coils. The chiller, with a rating of 1000W
nominal, was used to cool and recirculate water through the heat exchanger. The
chiller was set to 20°C for testing.
56
3.7.3.2. Pipe Flow Data Acquisition
Data acquisition was accomplished using a Keithley 2700 DMM/Data
Acquisition System paired with a Keithley 7708 Multiplexer Module. The Keithley
2700 was connected to a computer running Excelling software, available from
Keithley Instruments. The software allowed data logging directly into an Excel
spreadsheet. The acquired data included 23 thermocouple channels (inlet, outlet,
10 inside, 10 outside, ambient) as well as two voltage channels, which measured
the heater voltage and the voltage drop across the shunt resistor.
3.7.3.3. Pipe Flow Test Procedure
Before starting a set of tests, the system was first cleaned to avoid
contamination between fluids. This was accomplished by circulating water
through the test setup to dilute and remove any nanofluid remaining in the
plumbing of the setup. Water was run through the system with the heater and
chiller turned on for approximately 30 minutes. This was repeated until the water
remained clear for the full 30 minutes. This usually required 3 runs. Compressed
air was used to remove the last of the water so that the test would start with a
clean, dry system.
Once the setup was clean, the nanofluid was poured into the fluid
reservoir of the test setup and the suction, recycle and pipe return lines from the
test setup were placed into the reservoir. The pump was turned on full flow and
allowed to run until the fluid began to recirculate through the system. The heater
and chiller were then turned on and the fluid was allowed to run for several
57
minutes to ensure that the sample fluid was mixed properly. Next, the valves on
the T connection were adjusted to obtain the desired flow rate for that test. A
1000 mL beaker and a stop watch were used to calculate the volumetric flow rate
through the test section. The pipe return line was placed in the beaker, and the
stopwatch was used to time how long it took for the fluid to go from the 100 mL
mark to the 500 mL mark. Once the proper flow rate was reached, the Keithley
data acquisition system was started and the logged data was recorded to a new
worksheet. The test continued until the fluid temperature reached steady state,
which took approximately two to four hours, depending on the fluid. The inlet and
outlet temperatures were plotted in Excel in real time to observe the temperature
of the system and detect when the test setup had reached steady state. Once the
test was complete, the data acquisition was stopped and the heater was turned
off. The fluid was then run at the highest flow rate with the chiller on for several
minutes to cool the system before the chiller and pump were turned off.
This procedure was repeated for each of the 3 types of nanotubes tested.
The base fluid was tested twice, at 6 different flow rates.
Each of the AG
concentrations and the first two LHT concentrations were also tested at 6
different flow rates. It was determined that nothing was being gained by testing
at multiple flow rates, so to save time the last LHT and all of the HHT fluids were
only tested at one flow rate. The results and significance of these tests are
described in the following sections.
58
3.8. Results and Discussion
3.8.1. Materials Characterization
In order to fully understand the properties that make carbon nanotubes
unique both the microstructure and surface of nanotubes samples were
characterized.
The pristine (AG) nanotubes consist of a variety of carbon
configurations: helical, straight, nested, carbon blacks with narrow distribution of
diameters and lengths Figure (3.12). However two main configurations seem to
be dominant within the nanoconstituents: nested and straight carbon nanotubes.
Figure (3-12) Bright-field Images of Pristine Carbon Nanotubes (AG)
59
Figure (3-13) Models of Various Nanotubes Configurations.
The five major carbon constituents represented under the term carbon
nanotubes are graphically represented in Figure (3-13) to better depict their
physical structure.
Carbon blacks are nanometric spheres of agglomerated
carbon also recognized as soot.
Helical carbon nanotubes, also known as
nanocoils, consist of carbon nanotubes that have a configuration similar to that of
a DNA strand. A major constituent of the carbon nanotubes are straight carbon
nanotubes, which exist as a series of coaxial carbon cylinders surrounding a
central hollow tube.
The dominant species within carbon nanotubes include
bamboo and nested carbon nanotubes. The bamboo species are similar to that
of the straight carbon nanotubes, except that they are segmented along their
length. Nested carbon nanotubes have an orientation similar to that of a set of
stacked Dixie® cups with a hollow core and also referred to as fishbone type
carbon nanotubes [86].
60
Carbon at low temperatures exhibits only local molecular ordering. As
they are heat treated an increase in temperature results in the aromatic
molecules become stacked in a column structure. Further heat treatment causes
these columns to coalesce forming a distorted, wavy structure [82]. Surpassing a
temperature of 2500oC the distorted graphene layers of carbon become flattened
forming an aligned structure, and if the material is graphitic it will attain the
minimum interlayer spacing in the graphite order between graphene layers
Figure (3-14).
Figure (3-14) Carbon Plane Structure as a Function of Heat-Treatment
Temperature [82]
By analogy, after heat treating the pristine nanotubes to a temperature of
3000oC, graphene layers became straight, and minimum interlayer spacing was
reached for the PR-24 HHT.
As shown by a TEM micrograph Figure (3-15) the layers within the “Dixie
Cup” carbon nanotubes have coalesced following heat-treatment.
61
Figure (3-15) Bright-field Micrograph of “Dixie Cup” CNTs Structure
At this magnification the inclination angle of each “cup” is apparent.
Within each cup it can be seen that the localized ordering of the graphene planes
has been changed due to coalescence resulting in continuous planes.
The
stacking effect is shown through the use of a grey-scale. The walls of the
nanotubes are dark due to their high electronic density. The surrounding regions
are starkly lighter with low electronic densities.
At high-magnification, figure (3-16), the graphene layers appear very
straight without any disclination defects. However, there is no change in the
inclination angle to the central core axis. The edge of any pair of graphene layers
have been rounded encapsulating carbon planes’ exposed edge. This allows the
exposed graphene planes to attain a level of maximum structural stability.
62
Figure (3-16) High-Resolution Imaging of Localized Area of “Dixie
Cups” Structure
3.8.2. Raman Analysis
Raman analysis is sensitive for detecting variation in disorder and
crystallinity for carbon materials, and Figures (3-17) show the Raman spectra
recorded for AG-CNT, LHT-CNT, and HHT-CNT, as well as those after the ozone
treatment and the secondary CNT growth. AG-CNT, LHT-CNT and HHT-CNT
have the D-band peaks at 1357, 1356 and 1351 cm-1, and G-band peaks at
1599, 1589 and 1383 cm-1, respectively. The heat treatment at 1500 oC resulted
in an increase of G-band peak relative to D-band peak, and for that carried out at
63
2300 oC, a further large increase of the G/D peak ratio were identified, showing a
reduction in disorder and increase in crystallinity by the heat treatment, especially
by that carried out at the highest temperature
AG-CNT
LHT-CNT
HHT-CNT
Figure (3-17) Raman Spectra Records for AG, LHT, and HHT-CNT, as well
as those after the Ozone Treatment and the Secondary CNT Growth.
3.8.3. XRD Analysis
The buckypapers prepared using the three types of CNTs were
characterized by X-ray diffraction (XRD) using a diffractometer (Rigaku Ultima III)
with nickel-filtered Cu–Kb radiation (k =1.5406 A). The diffraction patterns were
taken at room temperature in the range of 10<2θ<80 using step scans. As it can
be seen, the peak density and sharpness of (002) plane of the CNTs increased
with increasing the heat treatment temperature. Interlayer spacing obtained from
the (002) peaks, was 0.3489, 0.3475 and 0.3422 nm for the AG-, LHT- and HHTCNT. The apparent crystallite size along the c axis, Lc, was calculated from the
64
(002) peak width in half density, and it was 3.2, 5.6, and 7.3 nm, respectively.
The XRD results are in agreement with the Raman analyses in that the heat
treatment increased crystallinity and degree of graphitization of the CNTs that
were utilized to construct the buckypapers.
AG-CNT
LHT-CNT
HHT-CNT
Figure (3-18) XRD Image of Field Emission Test
Each of the pristine carbon nanotubes was tested for electrical volume
resistivity measurement. The description of each of the nanotubes is provided in
Table (3-1).
Table (3-1) Description of Tested Carbon Nanotubes
ASI Sample
Description
AG Grade
As-grown nanotubes (processing temperature 1100°C)
LHT Grade
Nanotube after heat treatment temperature up to 1500°C
HHT Grade
Nanotube after heat treatment temperature to 3000°C
65
The results of the volume resistivity testing are presented in Figure (3-19).
There is a significant drop in the volume resistivity between the AG and LHT
nanotubes. However, the change in the temperature difference between the AG,
and LHT is only 400°C. This indicated an anomaly in the observed decrease in
resistivity. Further heat treatment of the nanotubes does not significantly reduce
the volume resistivity. If the volume resistivity were only a function of heat
treatment temperature, the values should result in a relatively linear decrease
with a sharp decrease after 2500°C. There is likely an additional factor in the
determination of volume resistivity which resides either in the surface contact or
Vol. Resistivity (ohm-cm /%vol)
deposition of a dielectric material.
108 PSI
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
AG
LHT
HHT
Effect of Heat Treatment Temperature
Figure (3-19) Volume Resistivity of as Received CNTs at 108psi
66
3.9. Thermal Conductivity of Nanofluid Samples
3.9.1. Static
k, measurements were first taken for 4
Static thermal conductivity,
different types of fluids. These were the base fluid and the base fluid with
different loadings of one of the three types of nanotubes. AG, LHT, and HHT
carbon nanotubes were used.
Each of these nanotubes were tested as
described before at different concentrations (0.2 wt%, 0.4 wt%, 0.6 wt%, 0.8
wt%, 1.0 wt%, and 1.2 wt %). Each of these types of nanotubes had different
crystallinity. The HHT nanotubes had the highest Crystallinity, followed by LHT
then AG.
Therefore, the results show how both the concentration and
crystallinity of the nanotubes affect the thermal conductivity of the fluid. The
results presented in Figure (3-20) are shown as percent improvement, as
calculated by equation (3.7). Where
of a particular nanofluid, and
is the steady state thermal conductivity
is the steady state thermal conductivity of the
base fluid.
K % improvement
100%
(3.7)
As shown in Figure (3-20), the thermal conductivity increases with
increasing nanotube concentration. The
k values of the AG and LHT fluids did
not change significantly after 0.8 wt%, while the
k value of the HHT fluids
continued to increase. Also, HHT had a larger improvement than LHT which had
67
a larger improvement than AG for all concentration tested.
Therefore, the
particles with greater crystallinity made nanofluids more thermally conductive.
Percent improvement in K
K Improvement for AG,LHT and HHT
35
30
25
20
15
AG
10
LHT
5
HHT
0
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
weight percent%
Figure (3-20) Percent Improvement in Thermal Conductivity
versus wt% Loading
Static testing was performed on three additional types of fluids. These
consisted of the same three nanotubes as before, which were first milled. Each
of the samples was milled in a ball shear machine for 30 minutes. This was done
to simulate the shearing that would be experienced in the dynamic shear testing.
Once the samples were milled the fluids were made and tested as before. Figure
(3-21) shows the percent improvement in
compared to the milled nanotubes.
68
k value of each type of nanotube
Percent improvement in K
HHT
35
30
25
20
15
HHT
10
HHT_Milled
5
0
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
weight percent %
(3-21a)
LHT
Percent improvement in K
16
14
12
10
LHT
8
6
LHT_Milled
4
2
0
0.20%
0.40%
0.60%
0.80%
1.00%
weight percent %
(3-21b)
69
1.20%
Percent improvement in K
AG
12
10
8
6
AG
4
AG_Milled
2
0
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
weight percent %
(3-21c)
Figure (3-21) Comparing Milled Samples to Non-milled Samples
of the Three CNTs Tested.
No significant change in percent improvement of k was observed between
the milled and non-milled samples of the LHT nanotubes. However, significant
change was observed in the milled HHT samples. The milled HHT fluids had a
lower improvement than the non-milled samples.
HHT has a much higher
crystallinity than LHT. This suggests that crystalline nanotubes are more brittle
and become very sensitive to any mechanical shearing.
These results also
indicated that HHT fluids might perform poorly in the dynamic shear test than
expected based on the static tests of the non-milled samples. Because HTT
sample is very crystalline but very brittle in which it is very sensitive to any
mechanical shear action. In case of AG sample, the milling has some effect in
reducing the overall thermal conductivity. Since the AG is made of turbostractic
carbons the layer are full of disclination defects and then sensitive to tension or
shear stresses.
70
3.9.2 Dynamic Shear
The shear rate ( ) of each test was calculated using equation (3.8) where
is the inside radius of the copper cylinder and
is the outside radius of the
aluminum cylinder. These are also the outer and inner surfaces of the fluid gab
section respectively.
is the angular velocity of the copper cylinder.
Figure (3-22) shows the rotating cylindrical system.
γ = 2ωo
α
(3.8)
α
where
(3.9)
Stationary cylinder (Aluminum)
Rotating cylinder (Copper)
Fluid gap
Angular velocity
ωo
Figure (3-22) Shows the Rotating Cylindrical System.
Heat flowed in a radial direction from the core heater to the water bath
through the global thermal resistance (RthGlobal) which consists of the inner
71
cylinder, the test fluid medium in the gap and the outer cylinder. The applied heat
flux (
) was calculated using equation (3.10)
(3.10)
Vsupply is the supply voltage to the heater and Relec_Heater is the electrical
resistance of the heater (116.29 ohms).
To determine the global thermal resistance we also have to take into
account the effect of radial convection. There will be convection at interface
between the fluid and, both inner cylinder and outside cylinder. This mechanism
is handled by Newtonian law:
(3.11)
(3.12)
The heat equation for cylindrical system
γ
(3.13)
Finally all of these effects (Eq. (3.11), (3.12) and (3.13)) will take part in the
fluid thermal resistance as follows:
72
(3.14)
Making the electrical analogy of this apparatus and considering that the heat
flux is constant on radius, we have the following global equation:
(3.15)
This global thermal resistance includes the fluid thermal resistance added to
aluminum and copper resistance as follows:
(3.16)
So equations (3.15) and (3.16) lead to the fluid thermal resistance calculation
from the following equation
(3.17)
=
The thermal resistance (
) of the fluid across the gab was calculated
for each shear rate using equation (3.17). Theater is the inner wall temperature,
Tcooler is the outer wall temperature, roCu and riCu is the outside and inside radius
of the copper cylinder respectively. roAl and riAl is the outside and inside radius
73
of the aluminum cylinder respectively. L is the height of the test section and kCu
and kAl are the thermal conductivities of copper and aluminum respectively.
Once again tests were first conducted on the base fluid and nanofluids
containing AG, LHT, and HHT nanotubes. First, the base fluid was tested and
analyzed using the method and equations described above. This was used as a
baseline to determine percent reduction in thermal resistance for the nanofluids
tested. Percent reduction in thermal resistance was the metric used to determine
the success in enhancing the heat transfer ability of the nanofluid. The steady
state experimental data were obtained for each sample (temperature, heater
voltage supply, and shear rate). This data was analyzed and is presented in
Figure (3-23), which shows the percent reduction in thermal resistance vs. shear
rate for LHT, HHT, and AG nanofluids at different concentrations.
Perecent reduction in Rth
LHT Nanofluids
70
60
50
0.20%
40
0.40%
0.60%
30
0.80%
20
1.00%
10
1.20%
0
0
150
200
300
400
Shear rate
(3-23a)
74
(s-1)
500
600
Percent reduction in Rth
HHT Nanofluids
60
50
0.20%
40
0.40%
30
0.60%
0.80%
20
1.00%
10
1.20%
0
0
150
200
300
400
500
600
Shear rate (s-1)
(3-23b)
Percent reduction in Rth
AG Nanofluids
35
30
0.20%
25
0.40%
20
0.60%
15
0.80%
10
1.00%
5
1.20%
0
0
150
200
300
400
500
600
Shear rate (s-1)
(3-23c)
Figure (3-23) Percent Reduction in Thermal Resistance versus
Concentration for AG, LHT, and HHT Nanofluids
75
It was observed that there was a large increase in percent reduction of
thermal resistance in the first step of rotation (shear rate= 150 s -1). For LHT and
HHT nanofluids, the thermal resistance showed little to no further change as the
shear rate was increased to 600 s-1. However, the percent reduction in thermal
resistance for AG did continue to increase as the shear rate was increased.
These charts also show that the larger the concentration of nanotubes, the
greater the percent reduction in thermal resistance.
This effect is most noticeable in the 0.2 and 0.4 wt% fluids. The effect
starts to die out around 0.8 wt% as all three fluids show negligible change or
even start to decrease in effectiveness around that concentration. The maximum
reduction in thermal resistance for LHT was about 63% and occurred at 1.2 wt%.
For HHT and AG nanofluids the 0.8 wt% showed the greatest enhancement.
The maximum enhancement for HHT was about 53% and for AG was about
32%. Figure (3-24) shows a better comparison of which nanofluid performed
best. It shows the percent reduction in thermal resistance of each concentration
of each nanofluid at a shear rate of 600 s-1.
76
Percent Reduction in Rth
70
Performance of Nanofluids at a shear rate of
600 s-1
60
50
LHT
40
30
HHT
20
AG
10
0
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
shear rates-1
Figure (3-24) Percent Reduction in Thermal Resistance at (600 s-1) Shear
Rate
It is clear from this chart that LHT performed best, followed by HHT then
AG. This is slightly different than the static tests, where HHT performed better
than LHT. This seems counterintuitive, until the data from the milled static tests in
taken into consideration. The HHT performed worse after being milled while the
LHT and AG did not. This suggests that the shear test acts as a mill and breaks
down the nanotubes. This theory was further investigated by testing nanofluid
samples where the nanotubes had been milled. These were made in the same
way that they were made for the static tests. Figure (3-25) shows the
performance comparison between the original samples and milled nanotubes
suspensions at (600 s-1) shear rate.
77
Percent Reduction in Rth
LHT vs LHT Milled at (600 s-1) shear rate
70
60
50
LHT
40
LHT_mill
30
20
10
0
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
Weight Percent
(3-25a)
HHT vs HHT Milled at (600 s-1) shear rate
Percent Reduction in Rth
50
40
HHT
30
HHT_mill
20
10
0
0.20%
0.40%
0.60%
0.80%
(3-25b)
78
1.00%
1.20%
Weight Percent
Percent Reduction in Rth
AG vs AG milled at (600 s-1) shear rate
35
30
AG
25
AG_mill
20
15
10
5
0
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
Weight Percent
(3-25c)
Figure (3-25) Comparison of Nanofluids with Milled Nanoparticles
versus Non-Milled Nanoparticles at (600 s-1) Shear Rate.
The milled nanofluids performed much worse than the non-milled
nanofluids. This confirms that when the nanotubes are subject to high shear and
break down, their thermal properties also suffer. However, this did not quite
explain why the LHT nanofluids performed better than the HHT ones. However,
the milling causes more shear than experienced in the testing. Also, based on
the geometry of the particles, HHT is more susceptible to breaking down.
Therefore, further testing was performed to assess the effects of high shear on
the nanotubes.
79
Raman testing was performed on the nanotubes to determine what affect
shear had on their crystallinity. Table (3-2)
Table (3-2) La Values from Raman Test
CNT Type
La value
AG
AG_HD
AG_shear
2.48685
2.659308
2.669935
LHT
LHT_HD
LHT_shear
3.985538
3.287278
3.77131
HHT
HHT_HD
HHT_shear
14.48747
7.950279
8.05352
Each of the types of nanotubes was tested in their as received state,
after milling, and after being run through a shear test. Each sample’s La value
was calculated to determine their crystallinity. None of the AG and LHT samples
showed any significant change in la value. This suggests that the crystallinity of
AG and LHT nanotubes does not change appreciably due to shear.
This
conclusion is supported by the dynamic shear and static test results. However,
the HHT samples showed significant change. The la values for the milled and
after shear sample were about 40% lower than the as received sample. This
means that exposure to high shear significantly decreases the crystallinity of
HHT nanotubes. Decreased crystallinity leads to decreased conductivity. This
explains why the milled HHT samples had lower conductivity than the non-milled
in the static tests. It also explains why the LHT nanofluids performed better than
80
the HHT nanofluids in the dynamic shear test. The HHT nanotubes broke down,
while the LHT nanotubes did not.
Based on the information from the static and dynamic testing, several
conclusions were made.
Higher concentrations of nanotubes lead to better
thermal performance. However, there appears to be a point, around 0.8 to 1.0
wt%, where the properties stop increasing and may even start to decrease. The
static data suggested that the crystallinity of the nanotubes affected the thermal
performance of the fluids. However, it was also seen that higher crystallinity
meant that the nanotubes were more subject to break down under shear. This
was confirmed by the dynamic shear testing. HHT broke down, so the LHT
nanofluids performed better, even though they did worse than HHT in the static
testing. The breakdown of nanotubes with high crystallinity was further confirmed
using Raman testing. Based on these results it was decided to continue the
testing using the pipe flow apparatus. This apparatus is much more similar to an
industry cooling setup. Therefore, it is useful in determining the practicality of the
nanofluids as coolants.
3.9.3. Pipe Flow
Nanofluids containing 0.2 wt% LHT and HHT, as well as the base fluid,
were tested at a flow rate of approximately 1.75 L/min. This corresponded to a
shear rate of 600 sec-1 which was the highest shear rate used in the dynamic
shear test. Higher flow rates gave better data when using the pipe flow apparatus
and the LHT and HHT results from the dynamic shear test changed negligibly
81
when going from low shear rates to high shear rates. Therefore, the fluids were
only tested at the 600 sec-1 shear rate. LHT and HHT were tested because they
showed the best results in the static and dynamic shear tests.
Higher
concentrations were made and tested. However, they started to become viscous
at 0.4 wt%, which caused problems with the test setup. However, the 0.2 wt%
solutions had negligible change in viscosity, so they were able to be tested
without problems.
Improvement in convective heat transfer coefficient (HTC) was used as
the metric to determine the effectiveness of each sample fluid as a coolant. The
HTC was calculated for each of the nanofluids, and then compared to the
calculated HTC of the Base fluid.
The following equations were used to make this calculation.
HTC =
(3-18)
Where A is the inside surface area of the pipe and
φ is the power going to the
heated test section. It was calculated using equation (3-19).
=
(3-19)
Where Vsupply is the voltage across the heater, Vdrop is the voltage drop
across the shunt resistor, and Rshunt is the electrical resistance of the shunt
resistor. ∆T is the temperature difference between the wall temperature and the
82
fluid temperature. The wall temperature was the average of the readings from
the 10 thermocouples on the outside of the pipe. The fluid temperature was the
average of the readings from the inlet and outlet thermocouples. Steady state
values were used for these averages. The test was allowed to run until the
temperature readings did not change for at least 20 minutes. These values were
then averaged and substituted into equations (3-18) and (3-19) to obtain the HTC
for the fluid. Figure (3-26) shows the percent improvement in HTC for LHT and
HHT. Using equation (3-20) to calculate the percent improvement of heat transfer
coefficient:
100 %
(3.20)
Percent improvement of HTC
Improvement in HTC
18.00
14.55 %
16.00
14.00
12.00
10.00
HHT
8.00
6.00
4.00
LHT
2.14 %
2.00
0.00
0.20%
WT%
Figure (3-26) Percent Improvement in Heat Transfer Coefficient at 0.2 wt%
Loading
83
This figure shows that while both nanofluids had better HTCs than the
base fluid, the LHT nanofluid showed a much greater improvement. It makes
sense that LHT performed better than HHT, since it did in the dynamic shear test.
However, instead of just doing a few percent better, as the LHT did for 2 wt% in
the dynamic shear, it did 12% better, a 7 fold increase.
There are a few
explanations for this. One is wetability. The wetability of HHT nanotubes is
much worse than LHT. This creates a thermal barrier around the particles which
hinders effective heat transfer. Also, as the Raman data showed, HHT particles
significantly break down under high shear. These both explain why LHT would
do better than HHT under dynamic, high shear situations. However, these are
both present in the dynamic shear, so the fact that LHT improved even more in
the pipe flow test, is not fully explained.
In order to explain this phenomena better, further testing was performed.
SEM images were taken of HHT nanotubes in three different conditions. These
were used to determine the extent of particle break down. They were tested
before any testing was performed, after being run through a dynamic shear test,
and after being run through a pipe flow test. The average length of nanotubes in
the untested samples was approximately 16 micrometers. The dynamic shear
sample had an average length of 14 micrometers, but also contained many fibers
with an average length of only 5 micro meters. This confirms what the Raman
data showed, that exposure to high shear breaks down HHT. Finally, the pipe
flow sample had an average length of only 5.6 micro meters. This is a major
84
reduction in size, showing that the extended exposure to high shear in the pipe
flow test breaks down the HHT particles even more than the dynamic shear test.
These results show that HHT breaks down even further than expected.
This is a possible explanation for why the HHT nanofluid did much worse than
the LHT nanofluid in the pipe flow test, even though LHT only performed a little
better in the dynamic shear test. It is likely that HHT had further break down
while LHT still had negligible break down.
That combined with the worse
wetability of HHT explain why LHT did better in the dynamic shear, and much
better in the pipe flow, while HHT did much better in the static testing.
85
CHAPTER IV
MODELING
The objective of this study is to build a model for predicting the thermal
conductivities of CNT nanofluids. In modeling it can easily see the effect of
changes of parameters; nanotubes loading, base fluid and nanotubes thermal
conductivities, nanotubes size, etc.
The Traditional composite models, such as Hamilton–Crosser, Maxwell
models fail to estimate the effective thermal conductivity of CNT nanofluids.
Various models have been formulated to explain the abnormal enhancement in
the thermal conductivity of CNT nanofluids such as Yu–Choi model, Xue model
and Nan model. Sastry et al. [91] developed a theoretical model based on threedimensional CNT chain formation (percolation) in the base liquid and the
corresponding thermal resistance network. The model considered random CNT
orientation and CNT-CNT interaction forming the percolating chain.
From the SEM pictures of nanotube suspension Figure (4-1), it can be
observed that an approximate porous arrangement is formed for the two phase
dispersion of nanotubes in the base fluid.
86
Figure (4-1) SEM Picture of Nanotube Suspension
This porous structure consist of hexagonal –like cells, the cells is formed
by an interconnection network of nanotubes, each side and corner of the
hexagon is formed by bundles of nanotubes.
In order to investigate this theoretically an analytical model is developed
based on Calmidi et al. model [72], in which a periodic two dimensional
hexagonal array is postulated, where the nanotubes bundles are the edges of
hexagons Figure (4.2), and then one-dimensional conduction analysis is
performed in the periodic structure in order to derive an analytical expression for
the conductivity. The assumption of one dimensional may not be true locally; but
globally the heat transfer is indeed one-dimensional. Since the structure is
periodic, it is convenient to consider a unit cell figure (4-3).
87
Unit Cell
CNT bundle
Figure (4-2) Hexagonal Mesh of CNT
Direction of Heat
Flux
2 rb
2b
v rb
rb
L
rb
Layer 3
rb
b
rb
rb
L
2L
Figure (4-3) Unit Cell of Hexagon Network
88
Layer 2
Layer 1
To determine the effective thermal conductivity, the unit cell can be
divided into three layers in series. The conductivity of each layer is derived
separately by applying parallel law of thermal resistance [72].
The extremely high thermal conductivity reported for CNT (2000 w/m-K)
and very high aspect ratio of CNTs (around 1000) makes a continuous path for
the heat to flow in the CNT network ligaments; however the ligament consists of
a bundle of CNTs in which there is considerable thermal resistance between
adjacent nanotubes. That leads to make the in-plane thermal conductivity of the
nanotubes bundle is about (10 w/m-K) and the through-plane thermal
conductivity of the bundle is about (1 w/m-K).
In layer 1, the solid (the nanotubes bundle) and the fluid phases are in
parallel. Their respective volumes are given by
(4.1)
(4.2)
“w” is the width in the third direction (perpendicular to the plane of the paper),
“rb” is the thickness of the hexagon side (the thickness of the nanotubes bundle)
and “b” is the thickness of the node (the nanotubes bundles interconnection).
The conductivity of the first layer:
……… (4.3)
89
(4.4)
(4.5)
RT1 is the thermal resistance of the first layer, ke1 is the thermal conductivity of
the first layer, kpt is the through-plane thermal conductivity in the nanotubes
bundle, kn is the thermal conductivity of the node (the nanotubes bundles
interconnection) and kf is the thermal conductivity of the base fluid.
For the second layer:
(4.6)
(4.7)
The conductivity of the second layer:
(4.8)
(4.9)
(4.10)
90
RT2 is the thermal resistance of the second layer and ke2 is the thermal
conductivity of the second layer.
For the third layer:
(4.11)
(4.12)
The conductivity of the third layer:
……… (4.13)
(4.14)
(4.15)
RT3 is the thermal resistance of the third layer and ke3 is the thermal conductivity
of the third layer, and
is in-plane thermal conductivity.
91
By combining the three layers which are in series, the effective thermal
conductivity of the unit cell can be written as
(4.16)
(4.17)
where
ke1, ke2 and ke3 are given by equations (5), (10) and (15) respectively
and L1,
L2 and L3 are the heights of the three layers in figure (4-3).
The concentration (volume fraction) is the ratio of nanotubes network volume to
the volume of the unit cell. For the assumed hexagonal geometry, it can be easily
shown to be
Conc. (%) =
(4.18)
92
thermal conductivity
enhancement %
AG
12
experimental
10
present model
8
6
4
2
0
0
0.2
0.4
0.6
0.8
1
1.2
0.8
1
1.2
weight %
(4-4a)
thermal conductivity
enhancement %
LHT
20
experimental
16
present model
12
8
4
0
0
0.2
0.4
0.6
weight %
(4-4b)
93
thermal conductivity
enhancement %
HHT
28
experimental
present model
24
20
16
12
8
4
0
0
0.2
0.4
0.6
weight %
0.8
1
1.2
(4-4c)
Figure (4-4) Compression between the Hexagonal Array Model and the
Experimental Results.
For the AG nanofluid, the thermal conductivity of the bundles is 5 w/m-K.
While for the LHT nanofluid is 9 w/m-K and for the HHT nanofluid is 12 w/m-K.
The thermal conductivity of the three types of CNT/ EG based nanofluids
predicting by this model (hexagonal array model) are compared with the
experimental results as shown in Figure (4-4). It is found that the present model
shows a reasonably good agreement with the experimental results.
Increasing the particle loading by adding more CNTs to the suspension
increase the numbers of CNTs in each ligament (the hexagonal side), which
increases the ligament thickness. In the same time the overlap between the
CNTs in the ligament is increased with the increasing of the particle loading, in
which the hexagonal side becomes shorter. In other words, the hexagonal
network becomes more compact.
94
CHAPTER V
CONCLUSIONS AND FUTURE RECOMMENDATIONS
5.1. Conclusions
A detailed experimental study was performed to analyze the effectiveness
of using AG, LHT, and HHT carbon nanotubes to enhance the heat transfer
ability of ethylene glycol. Static thermal conductivity was determined using a
transient hot wire method. Dynamic shear tests were conducted to determine
conductivity under dynamic conditions. Finally, pipe flow tests were performed to
determine the convective heat transfer coefficient.
These tests showed that
while the HHT nanofluids performed best under static conditions, with a 30%
improvement, the LHT nanofluids did best for the dynamic shear and pipe flow
test with over 60% and 14% improvement respectively.
Further testing was
performed to attempt to explain this unexpected result. Milled samples were
tested, which showed that HHT nanoparticles are subject to adverse results from
shear milling. Raman tests were also conducted. These showed that AG and
LHT nanoparticles had negligible change in crystallinity when subjected to high
shear.
However, HHT nanoparticles had approximately a 40% decrease in
crystallinity when subjected to milling or high shear from the dynamic shear or
95
pipe flow tests. This suggests that nanoparticles with high crystallinites, such as
HHT, tend to break down under shear and negatively affect the nanofluid’s
thermal properties. SEM images were also taken of the HHT nanoparticles. They
showed that the particles after pipe flow testing broke down even more than the
particles after dynamic shear testing.
This supports the Raman testing, and
explains why the LHT nanofluid did so much better than the HHT nanofluid in the
pipe flow test.
Higher concentrations that performed well in the static and
dynamic shear tests were not even able to be tested in the pipe flow apparatus
due to their high viscosity. This study suggests that low loadings of LHT carbon
nanofibers (0.2 wt% or less) in ethylene glycol can lead to great improvements in
heat transfer coefficient. These nanofluids could be used to greatly increase
cooling ability in practical industry applications.
From the static measurements, HHT nanofluids would be expected to be
the best for use as coolants.
However, since this study looked at dynamic
situations interesting discoveries were made. It was found that highly ordered
particles, such as HHT, do transfer heat better. However, they also tend to break
down while under high shear and have poor wetability which also decreases their
effectiveness.
Therefore, a balance between good thermal properties and
physical properties must be found. The particles should have enough wetability
to remain in suspension and not hinder heat transfer. They have to have good
thermal conductivity, but should not be too brittle and significantly break down
under high shear. LHT has a decent balance. It is ordered enough to have good
thermal conductivity. However, it is wetable enough to form a good suspension,
96
and not brittle enough to break down significantly. Further research should look
at finding particles that can form good suspensions, have high thermal
conductivity, but not be as ordered and brittle as to easily break down.
To model the thermal conductivity enhancement, we developed an
analytical model, based on experimental observation of CNT-liquid and CNTCNT interaction. The CNT dispersed in base fluid can form an extensive threedimensional CNT network that facilitates thermal transport, in which the CNT
bundles contact each other to form a hexagonal network. The thermal
conductivity enhancement of CNT nanofluid is attributed to the high thermal
conductivity and the high aspect ratio of carbon nanotube and its distribution in
the base fluid.
5.2. Future Recommendations

In pipe flow test, high concentration nanofluids were not able to be tested
due to the high viscosity, to investigate the nanofluid performance in pipe
flow test, apparatus setup need to be changed by increasing the pipe
diameter and increasing the power of the pump.

The thermal conductivity of nanofluids increases when the particle loading
increase, but the viscosity increases also with the particle loading increment.
Therefore, a balance between good thermal conductivity and viscosity should
also be investigated further.

The tests were performed in a specific temperature. Further studies should
investigate
on
nanofluid
behavior
97
at
different
temperatures.
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